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Sample records for stochastic phase-field model

  1. A stochastic phase-field model determined from molecular dynamics

    KAUST Repository

    von Schwerin, Erik

    2010-03-17

    The dynamics of dendritic growth of a crystal in an undercooled melt is determined by macroscopic diffusion-convection of heat and by capillary forces acting on the nanometer scale of the solid-liquid interface width. Its modelling is useful for instance in processing techniques based on casting. The phase-field method is widely used to study evolution of such microstructural phase transformations on a continuum level; it couples the energy equation to a phenomenological Allen-Cahn/Ginzburg-Landau equation modelling the dynamics of an order parameter determining the solid and liquid phases, including also stochastic fluctuations to obtain the qualitatively correct result of dendritic side branching. This work presents a method to determine stochastic phase-field models from atomistic formulations by coarse-graining molecular dynamics. It has three steps: (1) a precise quantitative atomistic definition of the phase-field variable, based on the local potential energy; (2) derivation of its coarse-grained dynamics model, from microscopic Smoluchowski molecular dynamics (that is Brownian or over damped Langevin dynamics); and (3) numerical computation of the coarse-grained model functions. The coarse-grained model approximates Gibbs ensemble averages of the atomistic phase-field, by choosing coarse-grained drift and diffusion functions that minimize the approximation error of observables in this ensemble average. © EDP Sciences, SMAI, 2010.

  2. A stochastic phase-field model determined from molecular dynamics

    KAUST Repository

    von Schwerin, Erik; Szepessy, Anders

    2010-01-01

    The dynamics of dendritic growth of a crystal in an undercooled melt is determined by macroscopic diffusion-convection of heat and by capillary forces acting on the nanometer scale of the solid-liquid interface width. Its modelling is useful for instance in processing techniques based on casting. The phase-field method is widely used to study evolution of such microstructural phase transformations on a continuum level; it couples the energy equation to a phenomenological Allen-Cahn/Ginzburg-Landau equation modelling the dynamics of an order parameter determining the solid and liquid phases, including also stochastic fluctuations to obtain the qualitatively correct result of dendritic side branching. This work presents a method to determine stochastic phase-field models from atomistic formulations by coarse-graining molecular dynamics. It has three steps: (1) a precise quantitative atomistic definition of the phase-field variable, based on the local potential energy; (2) derivation of its coarse-grained dynamics model, from microscopic Smoluchowski molecular dynamics (that is Brownian or over damped Langevin dynamics); and (3) numerical computation of the coarse-grained model functions. The coarse-grained model approximates Gibbs ensemble averages of the atomistic phase-field, by choosing coarse-grained drift and diffusion functions that minimize the approximation error of observables in this ensemble average. © EDP Sciences, SMAI, 2010.

  3. Stochastic-field cavitation model

    International Nuclear Information System (INIS)

    Dumond, J.; Magagnato, F.; Class, A.

    2013-01-01

    Nonlinear phenomena can often be well described using probability density functions (pdf) and pdf transport models. Traditionally, the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian “particles” or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and, in particular, to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. First, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations

  4. Stochastic-field cavitation model

    Science.gov (United States)

    Dumond, J.; Magagnato, F.; Class, A.

    2013-07-01

    Nonlinear phenomena can often be well described using probability density functions (pdf) and pdf transport models. Traditionally, the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian "particles" or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and, in particular, to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. First, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations.

  5. A cavitation model based on Eulerian stochastic fields

    Science.gov (United States)

    Magagnato, F.; Dumond, J.

    2013-12-01

    Non-linear phenomena can often be described using probability density functions (pdf) and pdf transport models. Traditionally the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian "particles" or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and in particular to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. Firstly, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations.

  6. Stochastic modelling of two-phase flows including phase change

    International Nuclear Information System (INIS)

    Hurisse, O.; Minier, J.P.

    2011-01-01

    Stochastic modelling has already been developed and applied for single-phase flows and incompressible two-phase flows. In this article, we propose an extension of this modelling approach to two-phase flows including phase change (e.g. for steam-water flows). Two aspects are emphasised: a stochastic model accounting for phase transition and a modelling constraint which arises from volume conservation. To illustrate the whole approach, some remarks are eventually proposed for two-fluid models. (authors)

  7. Compressible cavitation with stochastic field method

    Science.gov (United States)

    Class, Andreas; Dumond, Julien

    2012-11-01

    Non-linear phenomena can often be well described using probability density functions (pdf) and pdf transport models. Traditionally the simulation of pdf transport requires Monte-Carlo codes based on Lagrange particles or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic field method solving pdf transport based on Euler fields has been proposed which eliminates the necessity to mix Euler and Lagrange techniques or prescribed pdf assumptions. In the present work, part of the PhD Design and analysis of a Passive Outflow Reducer relying on cavitation, a first application of the stochastic field method to multi-phase flow and in particular to cavitating flow is presented. The application considered is a nozzle subjected to high velocity flow so that sheet cavitation is observed near the nozzle surface in the divergent section. It is demonstrated that the stochastic field formulation captures the wide range of pdf shapes present at different locations. The method is compatible with finite-volume codes where all existing physical models available for Lagrange techniques, presumed pdf or binning methods can be easily extended to the stochastic field formulation.

  8. Preliminary Phase Field Computational Model Development

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yulan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hu, Shenyang Y. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Xu, Ke [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Suter, Jonathan D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); McCloy, John S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnson, Bradley R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ramuhalli, Pradeep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2014-12-15

    experiments, special experimental methods were devised to create similar boundary conditions in the iron films. Preliminary MFM studies conducted on single and polycrystalline iron films with small sub-areas created with focused ion beam have correlated quite well qualitatively with phase-field simulations. However, phase-field model dimensions are still small relative to experiments thus far. We are in the process of increasing the size of the models and decreasing specimen size so both have identical dimensions. Ongoing research is focused on validation of the phase-field model. Validation is being accomplished through comparison with experimentally obtained MFM images (in progress), and planned measurements of major hysteresis loops and first order reversal curves. Extrapolation of simulation sizes to represent a more stochastic bulk-like system will require sampling of various simulations (i.e., with single non-magnetic defect, single magnetic defect, single grain boundary, single dislocation, etc.) with distributions of input parameters. These outputs can then be compared to laboratory magnetic measurements and ultimately to simulate magnetic Barkhausen noise signals.

  9. Impact of wave phase jumps on stochastic heating

    International Nuclear Information System (INIS)

    Zasenko, V.I.; Zagorodny, A.G.; Cherniak, O.M.

    2016-01-01

    Interaction of charged particles with fields of random waves brings about known effects of stochastic acceleration and heating. Jumps of wave phases can increase the intensity of these processes substantially. Numerical simulation of particle heating and acceleration by waves with regular phases, waves with jumping phase and stochastic electric field impulses is performed. Comparison of the results shows that to some extent an impact of phase jumps is similar to the action of separate field impulses. Jumps of phase not only increase the intensity of resonant particle heating but involves in this process non-resonant particles from a wide range of initial velocities

  10. Stochastic population oscillations in spatial predator-prey models

    International Nuclear Information System (INIS)

    Taeuber, Uwe C

    2011-01-01

    It is well-established that including spatial structure and stochastic noise in models for predator-prey interactions invalidates the classical deterministic Lotka-Volterra picture of neutral population cycles. In contrast, stochastic models yield long-lived, but ultimately decaying erratic population oscillations, which can be understood through a resonant amplification mechanism for density fluctuations. In Monte Carlo simulations of spatial stochastic predator-prey systems, one observes striking complex spatio-temporal structures. These spreading activity fronts induce persistent correlations between predators and prey. In the presence of local particle density restrictions (finite prey carrying capacity), there exists an extinction threshold for the predator population. The accompanying continuous non-equilibrium phase transition is governed by the directed-percolation universality class. We employ field-theoretic methods based on the Doi-Peliti representation of the master equation for stochastic particle interaction models to (i) map the ensuing action in the vicinity of the absorbing state phase transition to Reggeon field theory, and (ii) to quantitatively address fluctuation-induced renormalizations of the population oscillation frequency, damping, and diffusion coefficients in the species coexistence phase.

  11. Stochastic inflation in phase space: is slow roll a stochastic attractor?

    Energy Technology Data Exchange (ETDEWEB)

    Grain, Julien [Institut d' Astrophysique Spatiale, UMR8617, CNRS, Univ. Paris Sud, Université Paris-Saclay, Bt. 121, Orsay, F-91405 (France); Vennin, Vincent, E-mail: julien.grain@ias.u-psud.fr, E-mail: vincent.vennin@port.ac.uk [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO13FX (United Kingdom)

    2017-05-01

    An appealing feature of inflationary cosmology is the presence of a phase-space attractor, ''slow roll'', which washes out the dependence on initial field velocities. We investigate the robustness of this property under backreaction from quantum fluctuations using the stochastic inflation formalism in the phase-space approach. A Hamiltonian formulation of stochastic inflation is presented, where it is shown that the coarse-graining procedure—where wavelengths smaller than the Hubble radius are integrated out—preserves the canonical structure of free fields. This means that different sets of canonical variables give rise to the same probability distribution which clarifies the literature with respect to this issue. The role played by the quantum-to-classical transition is also analysed and is shown to constrain the coarse-graining scale. In the case of free fields, we find that quantum diffusion is aligned in phase space with the slow-roll direction. This implies that the classical slow-roll attractor is immune to stochastic effects and thus generalises to a stochastic attractor regardless of initial conditions, with a relaxation time at least as short as in the classical system. For non-test fields or for test fields with non-linear self interactions however, quantum diffusion and the classical slow-roll flow are misaligned. We derive a condition on the coarse-graining scale so that observational corrections from this misalignment are negligible at leading order in slow roll.

  12. Effective-field theory for dynamic phase diagrams of the kinetic spin-3/2 Blume–Capel model under a time oscillating longitudinal field

    Energy Technology Data Exchange (ETDEWEB)

    Ertaş, Mehmet [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Kocakaplan, Yusuf [Institute of Science, Erciyes University, 38039 Kayseri (Turkey); Keskin, Mustafa, E-mail: keskin@erciyes.edu.tr [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

    2013-12-15

    Dynamic phase diagrams are presented for the kinetic spin-3/2 Blume–Capel model under a time oscillating longitudinal field by use of the effective-field theory with correlations. The dynamic equation of the average magnetization is obtained for the square lattice by utilizing the Glauber-type stochastic process. Dynamic phase diagrams are presented in the reduced temperature and the magnetic field amplitude plane. We also investigated the effect of longitudinal field frequency. Finally, the discussion and comparison of the phase diagrams are given. - Highlights: • Dynamic behaviors in the spin-3/2 Blume–Capel system is investigated by the effective-field theory based on the Glauber-type stochastic dynamics. • The dynamic phase transitions and dynamic phase diagrams are obtained. • The effects of the longitudinal field frequency on the dynamic phase diagrams of the system are investigated. • Dynamic phase diagrams exhibit several ordered phases, coexistence phase regions and several critical points as well as a re-entrant behavior.

  13. Effective-field theory for dynamic phase diagrams of the kinetic spin-3/2 Blume–Capel model under a time oscillating longitudinal field

    International Nuclear Information System (INIS)

    Ertaş, Mehmet; Kocakaplan, Yusuf; Keskin, Mustafa

    2013-01-01

    Dynamic phase diagrams are presented for the kinetic spin-3/2 Blume–Capel model under a time oscillating longitudinal field by use of the effective-field theory with correlations. The dynamic equation of the average magnetization is obtained for the square lattice by utilizing the Glauber-type stochastic process. Dynamic phase diagrams are presented in the reduced temperature and the magnetic field amplitude plane. We also investigated the effect of longitudinal field frequency. Finally, the discussion and comparison of the phase diagrams are given. - Highlights: • Dynamic behaviors in the spin-3/2 Blume–Capel system is investigated by the effective-field theory based on the Glauber-type stochastic dynamics. • The dynamic phase transitions and dynamic phase diagrams are obtained. • The effects of the longitudinal field frequency on the dynamic phase diagrams of the system are investigated. • Dynamic phase diagrams exhibit several ordered phases, coexistence phase regions and several critical points as well as a re-entrant behavior

  14. Stochastic inflation: Quantum phase-space approach

    International Nuclear Information System (INIS)

    Habib, S.

    1992-01-01

    In this paper a quantum-mechanical phase-space picture is constructed for coarse-grained free quantum fields in an inflationary universe. The appropriate stochastic quantum Liouville equation is derived. Explicit solutions for the phase-space quantum distribution function are found for the cases of power-law and exponential expansions. The expectation values of dynamical variables with respect to these solutions are compared to the corresponding cutoff regularized field-theoretic results (we do not restrict ourselves only to left-angle Φ 2 right-angle). Fair agreement is found provided the coarse-graining scale is kept within certain limits. By focusing on the full phase-space distribution function rather than a reduced distribution it is shown that the thermodynamic interpretation of the stochastic formalism faces several difficulties (e.g., there is no fluctuation-dissipation theorem). The coarse graining does not guarantee an automatic classical limit as quantum correlations turn out to be crucial in order to get results consistent with standard quantum field theory. Therefore, the method does not by itself constitute an explanation of the quantum to classical transition in the early Universe. In particular, we argue that the stochastic equations do not lead to decoherence

  15. A new stochastic cellular automaton model on traffic flow and its jamming phase transition

    International Nuclear Information System (INIS)

    Sakai, Satoshi; Nishinari, Katsuhiro; Iida, Shinji

    2006-01-01

    A general stochastic traffic cellular automaton (CA) model, which includes the slow-to-start effect and driver's perspective, is proposed in this paper. It is shown that this model includes well-known traffic CA models such as the Nagel-Schreckenberg model, the quick-start model and the slow-to-start model as specific cases. Fundamental diagrams of this new model clearly show metastable states around the critical density even when the stochastic effect is present. We also obtain analytic expressions of the phase transition curve in phase diagrams by using approximate flow-density relations at boundaries. These phase transition curves are in excellent agreement with numerical results

  16. Modelling of diesel spray flames under engine-like conditions using an accelerated Eulerian Stochastic Field method

    DEFF Research Database (Denmark)

    Pang, Kar Mun; Jangi, Mehdi; Bai, Xue-Song

    2018-01-01

    This paper aims to simulate diesel spray flames across a wide range of engine-like conditions using the Eulerian Stochastic Field probability density function (ESF-PDF) model. The ESF model is coupled with the Chemistry Coordinate Mapping approach to expedite the calculation. A convergence study...... is carried out for a number of stochastic fields at five different conditions, covering both conventional diesel combustion and low-temperature combustion regimes. Ignition delay time, flame lift-off length as well as distributions of temperature and various combustion products are used to evaluate...... the performance of the model. The peak values of these properties generated using thirty-two stochastic fields are found to converge, with a maximum relative difference of 27% as compared to those from a greater number of stochastic fields. The ESF-PDF model with thirty-two stochastic fields performs reasonably...

  17. Stochastic quantization of field theories on the lattice and supersymmetrical models

    International Nuclear Information System (INIS)

    Aldazabal, Gerardo.

    1984-01-01

    Several aspects of the stochastic quantization method are considered. Specifically, field theories on the lattice and supersymmetrical models are studied. A non-linear sigma model is studied firstly, and it is shown that it is possible to obtain evolution equations written directly for invariant quantities. These ideas are generalized to obtain Langevin equations for the Wilson loops of non-abelian lattice gauge theories U (N) and SU (N). In order to write these equations, some different ways of introducing the constraints which the fields must satisfy are discussed. It is natural to have a strong coupling expansion in these equations. The correspondence with quantum field theory is established, and it is noticed that at all orders in the perturbation theory, Langevin equations reduce to Schwinger-Dyson equations. From another point of view, stochastic quantization is applied to large N matrix models on the lattice. As a result, a simple and systematic way of building reduced models is found. Referring to stochastic quantization in supersymmetric theories, a simple supersymmetric model is studied. It is shown that it is possible to write an evolution equation for the superfield wich leads to quantum field theory results in equilibrium. As the Langevin equation preserves supersymmetry, the property of dimensional reduction known for the quantum model is shown to be valid at all times. (M.E.L.) [es

  18. Stochastic Still Water Response Model

    DEFF Research Database (Denmark)

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2002-01-01

    In this study a stochastic field model for the still water loading is formulated where the statistics (mean value, standard deviation, and correlation) of the sectional forces are obtained by integration of the load field over the relevant part of the ship structure. The objective of the model is...... out that an important parameter of the stochastic cargo field model is the mean number of containers delivered by each customer.......In this study a stochastic field model for the still water loading is formulated where the statistics (mean value, standard deviation, and correlation) of the sectional forces are obtained by integration of the load field over the relevant part of the ship structure. The objective of the model...... is to establish the stochastic load field conditional on a given draft and trim of the vessel. The model contributes to a realistic modelling of the stochastic load processes to be used in a reliability evaluation of the ship hull. Emphasis is given to container vessels. The formulation of the model for obtaining...

  19. BRS symmetry in stochastic quantization of the gravitational field

    International Nuclear Information System (INIS)

    Nakazawa, Naohito.

    1989-12-01

    We study stochastic quantization of gravity in terms of a BRS invariant canonical operator formalism. By introducing artificially canonical momentum variables for the original field variables, a canonical formulation of stochastic quantization is proposed in a sense that the Fokker-Planck hamiltonian is the generator of the fictitious time translation. Then we show that there exists a nilpotent BRS symmetry in an enlarged phase space for gravity (in general, for the first-class constrained systems). The stochastic action of gravity includes explicitly an unique De Witt's type superspace metric which leads to a geometrical interpretation of quantum gravity analogous to nonlinear σ-models. (author)

  20. Guidelines for the formulation of Lagrangian stochastic models for particle simulations of single-phase and dispersed two-phase turbulent flows

    Science.gov (United States)

    Minier, Jean-Pierre; Chibbaro, Sergio; Pope, Stephen B.

    2014-11-01

    In this paper, we establish a set of criteria which are applied to discuss various formulations under which Lagrangian stochastic models can be found. These models are used for the simulation of fluid particles in single-phase turbulence as well as for the fluid seen by discrete particles in dispersed turbulent two-phase flows. The purpose of the present work is to provide guidelines, useful for experts and non-experts alike, which are shown to be helpful to clarify issues related to the form of Lagrangian stochastic models. A central issue is to put forward reliable requirements which must be met by Lagrangian stochastic models and a new element brought by the present analysis is to address the single- and two-phase flow situations from a unified point of view. For that purpose, we consider first the single-phase flow case and check whether models are fully consistent with the structure of the Reynolds-stress models. In the two-phase flow situation, coming up with clear-cut criteria is more difficult and the present choice is to require that the single-phase situation be well-retrieved in the fluid-limit case, elementary predictive abilities be respected and that some simple statistical features of homogeneous fluid turbulence be correctly reproduced. This analysis does not address the question of the relative predictive capacities of different models but concentrates on their formulation since advantages and disadvantages of different formulations are not always clear. Indeed, hidden in the changes from one structure to another are some possible pitfalls which can lead to flaws in the construction of practical models and to physically unsound numerical calculations. A first interest of the present approach is illustrated by considering some models proposed in the literature and by showing that these criteria help to assess whether these Lagrangian stochastic models can be regarded as acceptable descriptions. A second interest is to indicate how future

  1. Guidelines for the formulation of Lagrangian stochastic models for particle simulations of single-phase and dispersed two-phase turbulent flows

    International Nuclear Information System (INIS)

    Minier, Jean-Pierre; Chibbaro, Sergio; Pope, Stephen B.

    2014-01-01

    In this paper, we establish a set of criteria which are applied to discuss various formulations under which Lagrangian stochastic models can be found. These models are used for the simulation of fluid particles in single-phase turbulence as well as for the fluid seen by discrete particles in dispersed turbulent two-phase flows. The purpose of the present work is to provide guidelines, useful for experts and non-experts alike, which are shown to be helpful to clarify issues related to the form of Lagrangian stochastic models. A central issue is to put forward reliable requirements which must be met by Lagrangian stochastic models and a new element brought by the present analysis is to address the single- and two-phase flow situations from a unified point of view. For that purpose, we consider first the single-phase flow case and check whether models are fully consistent with the structure of the Reynolds-stress models. In the two-phase flow situation, coming up with clear-cut criteria is more difficult and the present choice is to require that the single-phase situation be well-retrieved in the fluid-limit case, elementary predictive abilities be respected and that some simple statistical features of homogeneous fluid turbulence be correctly reproduced. This analysis does not address the question of the relative predictive capacities of different models but concentrates on their formulation since advantages and disadvantages of different formulations are not always clear. Indeed, hidden in the changes from one structure to another are some possible pitfalls which can lead to flaws in the construction of practical models and to physically unsound numerical calculations. A first interest of the present approach is illustrated by considering some models proposed in the literature and by showing that these criteria help to assess whether these Lagrangian stochastic models can be regarded as acceptable descriptions. A second interest is to indicate how future

  2. Stochastic geometry, spatial statistics and random fields models and algorithms

    CERN Document Server

    2015-01-01

    Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.

  3. Modeling of Eutectic Formation in Al-Si Alloy Using A Phase-Field Method

    Directory of Open Access Journals (Sweden)

    Ebrahimi Z.

    2017-12-01

    Full Text Available We have utilized a phase-field model to investigate the evolution of eutectic silicon in Al-Si alloy. The interfacial fluctuations are included into a phase-field model of two-phase solidification, as stochastic noise terms and their dominant role in eutectic silicon formation is discussed. We have observed that silicon spherical particles nucleate on the foundation of primary aluminum phase and their nucleation continues on concentric rings, through the Al matrix. The nucleation of silicon particles is attributed to the inclusion of fluctuations into the phase-field equations. The simulation results have shown needle-like, fish-bone like and flakes of silicon phase by adjusting the noise coefficients to larger values. Moreover, the role of primary Al phase on nucleation of silicon particles in Al-Si alloy is elaborated. We have found that the addition of fluctuations plays the role of modifiers in our simulations and is essential for phase-field modeling of eutectic growth in Al-Si system. The simulated finger-like Al phases and spherical Si particles are very similar to those of experimental eutectic growth in modified Al-Si alloy.

  4. Stochastic disk dynamo as a model of reversals of the Earth's magnetic field

    International Nuclear Information System (INIS)

    Ito, H.M.

    1988-01-01

    A stochastic model is given of a system composed of N similar disk dynamos interacting with one another. The time evolution of the system is governed by a master equation of the class introduced by van Kampen as relevant to stochastic macrosystems. In the model, reversals of the Earth's magnetic field are regarded as large deviations caused by a small random force of O(N/sup -1/2/) from one of the field polarities to the other. Reversal processes are studied by simulation, which shows that the model explains well the activities of the paleomagnetic field inclusive of statistical laws of the reversal sequence and the intensity distribution. Comparison are made between the model and dynamical disk dynamo models

  5. Dynamic phase transition in the kinetic spin-1 Blume-Capel model: Phase diagrams in the temperature and crystal-field interaction plane

    International Nuclear Information System (INIS)

    Keskin, M.; Canko, O.; Temizer, U.

    2007-01-01

    Within a mean-field approach, the stationary states of the kinetic spin-1 Blume-Capel model in the presence of a time-dependent oscillating external magnetic field is studied. The Glauber-type stochastic dynamics is used to describe the time evolution of the system and obtain the mean-field dynamic equation of motion. The dynamic phase-transition points are calculated and phase diagrams are presented in the temperature and crystal-field interaction plane. According to the values of the magnetic field amplitude, three fundamental types of phase diagrams are found: One exhibits a dynamic tricritical point, while the other two exhibit a dynamic zero-temperature critical point

  6. Dynamic phase transition in the kinetic spin-32 Blume-Capel model: Phase diagrams in the temperature and crystal-field interaction plane

    International Nuclear Information System (INIS)

    Keskin, Mustafa; Canko, Osman; Deviren, Bayram

    2007-01-01

    We analyze, within a mean-field approach, the stationary states of the kinetic spin-32 Blume-Capel (BC) model by the Glauber-type stochastic dynamics and subject to a time-dependent oscillating external magnetic field. The dynamic phase transition (DPT) points are obtained by investigating the behavior of the dynamic magnetization as a function of temperature and as well as calculating the Liapunov exponent. Phase diagrams are constructed in the temperature and crystal-field interaction plane. We find five fundamental types of phase diagrams for the different values of the reduced magnetic field amplitude parameter (h) in which they present a disordered, two ordered phases and the coexistences phase regions. The phase diagrams also exhibit a dynamic double-critical end point for 0 5.06

  7. Intermittent dislocation density fluctuations in crystal plasticity from a phase-field crystal model

    DEFF Research Database (Denmark)

    Tarp, Jens M.; Angheluta, Luiza; Mathiesen, Joachim

    2014-01-01

    Plastic deformation mediated by collective dislocation dynamics is investigated in the two-dimensional phase-field crystal model of sheared single crystals. We find that intermittent fluctuations in the dislocation population number accompany bursts in the plastic strain-rate fluctuations...... propose a simple stochastic model of dislocation reaction kinetics that is able to capture these statistical properties of the dislocation density fluctuations as a function of shear rate....

  8. Background field method for nonlinear σ-model in stochastic quantization

    International Nuclear Information System (INIS)

    Nakazawa, Naohito; Ennyu, Daiji

    1988-01-01

    We formulate the background field method for the nonlinear σ-model in stochastic quantization. We demonstrate a one-loop calculation for a two-dimensional non-linear σ-model on a general riemannian manifold based on our formulation. The formulation is consistent with the known results in ordinary quantization. As a simple application, we also analyse the multiplicative renormalization of the O(N) nonlinear σ-model. (orig.)

  9. Stochastic field theory and finite-temperature supersymmetry

    International Nuclear Information System (INIS)

    Ghosh, P.; Bandyopadhyay, P.

    1988-01-01

    The finite-temperature behavior of supersymmetry is considered from the viewpoint of stochastic field theory. To this end, it is considered that Nelson's stochastic mechanics may be generalized to the quantization of a Fermi field when the classical analog of such a field is taken to be a scalar nonlocal field where the internal space is anisotropic in nature such that when quantized this gives rise to two internal helicities corresponding to fermion and antifermion. Stochastic field theory at finite temperature is then formulated from stochastic mechanics which incorporates Brownian motion in the external space as well as in the internal space of a particle. It is shown that when the anisotropy of the internal space is suppressed so that the internal time ξ 0 vanishes and the internal space variables are integrated out one has supersymmetry at finite temperature. This result is true for T = 0, also. However, at this phase equilibrium will be destroyed. Thus for a random process van Hove's result involving quantum mechanical operators, i.e., that when supersymmetry remains unbroken at T = 0 it will also remain unbroken at Tnot =0, occurs. However, this formalism indicates that when at T = 0 broken supersymmetry results, supersymmetry may be restored at a critical temperature T/sub c/

  10. Stochastic spin-one massive field

    International Nuclear Information System (INIS)

    Lim, S.C.

    1984-01-01

    Stochastic quantization schemes of Nelson and Parisi and Wu are applied to a spin-one massive field. Unlike the scalar case Nelson's stochastic spin-one massive field cannot be identified with the corresponding euclidean field even if the fourth component of the euclidean coordinate is taken as equal to the real physical time. In the Parisi-Wu quantization scheme the stochastic Proca vector field has a similar property as the scalar field; which has an asymptotically stationary part and a transient part. The large equal-time limit of the expectation values of the stochastic Proca field are equal to the expectation values of the corresponding euclidean field. In the Stueckelberg formalism the Parisi-Wu scheme gives rise to a stochastic vector field which differs from the massless gauge field in that the gauge cannot be fixed by the choice of boundary condition. (orig.)

  11. Rapid core field variations during the satellite era: Investigations using stochastic process based field models

    DEFF Research Database (Denmark)

    Finlay, Chris; Olsen, Nils; Gillet, Nicolas

    We present a new ensemble of time-dependent magnetic field models constructed from satellite and observatory data spanning 1997-2013 that are compatible with prior information concerning the temporal spectrum of core field variations. These models allow sharper field changes compared to tradition...... physical hypotheses can be tested by asking questions of the entire ensemble of core field models, rather than by interpreting any single model.......We present a new ensemble of time-dependent magnetic field models constructed from satellite and observatory data spanning 1997-2013 that are compatible with prior information concerning the temporal spectrum of core field variations. These models allow sharper field changes compared to traditional...... regularization methods based on minimizing the square of second or third time derivative. We invert satellite and observatory data directly by adopting the external field and crustal field modelling framework of the CHAOS model, but apply the stochastic process method of Gillet et al. (2013) to the core field...

  12. Stochastic models: theory and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2008-03-01

    Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.

  13. Renormalization in the stochastic quantization of field theories

    International Nuclear Information System (INIS)

    Brunelli, J.C.

    1991-01-01

    In the stochastic quantization scheme of Parisi and Wu the renormalization of the stochastic theory of some models in field theory is studied. Following the path integral approach for stochastic process the 1/N expansion of the non linear sigma model is performed and, using a Ward identity obtained, from a BRS symmetry of the effective action of this formulation. It is shown the renormalizability of the model. Using the Langevin approach for stochastic process the renormalizability of the massive Thirring model is studied showing perturbatively the vanishing of the renormalization group's beta functions at finite fictitious time. (author)

  14. Stochastic quantization of Proca field

    International Nuclear Information System (INIS)

    Lim, S.C.

    1981-03-01

    We discuss the complications that arise in the application of Nelson's stochastic quantization scheme to classical Proca field. One consistent way to obtain spin-one massive stochastic field is given. It is found that the result of Guerra et al on the connection between ground state stochastic field and the corresponding Euclidean-Markov field extends to the spin-one case. (author)

  15. Stochastic massless fields I: Integer spin

    International Nuclear Information System (INIS)

    Lim, S.C.

    1981-04-01

    Nelson's stochastic quantization scheme is applied to classical massless tensor potential in ''Coulomb'' gauge. The relationship between stochastic potential field in various gauges is discussed using the case of vector potential as an illustration. It is possible to identify the Euclidean tensor potential with the corresponding stochastic field in physical Minkowski space-time. Stochastic quantization of massless fields can also be carried out in terms of field strength tensors. An example of linearized stochastic gravitational field in vacuum is given. (author)

  16. Stochastic biomathematical models with applications to neuronal modeling

    CERN Document Server

    Batzel, Jerry; Ditlevsen, Susanne

    2013-01-01

    Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

  17. Dynamic phase transition in the kinetic spin-32 Blume-Capel model: Phase diagrams in the temperature and crystal-field interaction plane

    Energy Technology Data Exchange (ETDEWEB)

    Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)]. E-mail: keskin@erciyes.edu.tr; Canko, Osman [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Deviren, Bayram [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

    2007-06-15

    We analyze, within a mean-field approach, the stationary states of the kinetic spin-32 Blume-Capel (BC) model by the Glauber-type stochastic dynamics and subject to a time-dependent oscillating external magnetic field. The dynamic phase transition (DPT) points are obtained by investigating the behavior of the dynamic magnetization as a function of temperature and as well as calculating the Liapunov exponent. Phase diagrams are constructed in the temperature and crystal-field interaction plane. We find five fundamental types of phase diagrams for the different values of the reduced magnetic field amplitude parameter (h) in which they present a disordered, two ordered phases and the coexistences phase regions. The phase diagrams also exhibit a dynamic double-critical end point for 05.06.

  18. Stochastic Kuramoto oscillators with discrete phase states

    Science.gov (United States)

    Jörg, David J.

    2017-09-01

    We present a generalization of the Kuramoto phase oscillator model in which phases advance in discrete phase increments through Poisson processes, rendering both intrinsic oscillations and coupling inherently stochastic. We study the effects of phase discretization on the synchronization and precision properties of the coupled system both analytically and numerically. Remarkably, many key observables such as the steady-state synchrony and the quality of oscillations show distinct extrema while converging to the classical Kuramoto model in the limit of a continuous phase. The phase-discretized model provides a general framework for coupled oscillations in a Markov chain setting.

  19. Stochastic Kuramoto oscillators with discrete phase states.

    Science.gov (United States)

    Jörg, David J

    2017-09-01

    We present a generalization of the Kuramoto phase oscillator model in which phases advance in discrete phase increments through Poisson processes, rendering both intrinsic oscillations and coupling inherently stochastic. We study the effects of phase discretization on the synchronization and precision properties of the coupled system both analytically and numerically. Remarkably, many key observables such as the steady-state synchrony and the quality of oscillations show distinct extrema while converging to the classical Kuramoto model in the limit of a continuous phase. The phase-discretized model provides a general framework for coupled oscillations in a Markov chain setting.

  20. Diffusive processes in a stochastic magnetic field

    International Nuclear Information System (INIS)

    Wang, H.; Vlad, M.; Vanden Eijnden, E.; Spineanu, F.; Misguich, J.H.; Balescu, R.

    1995-01-01

    The statistical representation of a fluctuating (stochastic) magnetic field configuration is studied in detail. The Eulerian correlation functions of the magnetic field are determined, taking into account all geometrical constraints: these objects form a nondiagonal matrix. The Lagrangian correlations, within the reasonable Corrsin approximation, are reduced to a single scalar function, determined by an integral equation. The mean square perpendicular deviation of a geometrical point moving along a perturbed field line is determined by a nonlinear second-order differential equation. The separation of neighboring field lines in a stochastic magnetic field is studied. We find exponentiation lengths of both signs describing, in particular, a decay (on the average) of any initial anisotropy. The vanishing sum of these exponentiation lengths ensures the existence of an invariant which was overlooked in previous works. Next, the separation of a particle's trajectory from the magnetic field line to which it was initially attached is studied by a similar method. Here too an initial phase of exponential separation appears. Assuming the existence of a final diffusive phase, anomalous diffusion coefficients are found for both weakly and strongly collisional limits. The latter is identical to the well known Rechester-Rosenbluth coefficient, which is obtained here by a more quantitative (though not entirely deductive) treatment than in earlier works

  1. Stochastic Subspace Modelling of Turbulence

    DEFF Research Database (Denmark)

    Sichani, Mahdi Teimouri; Pedersen, B. J.; Nielsen, Søren R.K.

    2009-01-01

    positive definite cross-spectral density matrix a frequency response matrix is constructed which determines the turbulence vector as a linear filtration of Gaussian white noise. Finally, an accurate state space modelling method is proposed which allows selection of an appropriate model order......, and estimation of a state space model for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modelling method.......Turbulence of the incoming wind field is of paramount importance to the dynamic response of civil engineering structures. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper...

  2. Fractional Stochastic Field Theory

    Science.gov (United States)

    Honkonen, Juha

    2018-02-01

    Models describing evolution of physical, chemical, biological, social and financial processes are often formulated as differential equations with the understanding that they are large-scale equations for averages of quantities describing intrinsically random processes. Explicit account of randomness may lead to significant changes in the asymptotic behaviour (anomalous scaling) in such models especially in low spatial dimensions, which in many cases may be captured with the use of the renormalization group. Anomalous scaling and memory effects may also be introduced with the use of fractional derivatives and fractional noise. Construction of renormalized stochastic field theory with fractional derivatives and fractional noise in the underlying stochastic differential equations and master equations and the interplay between fluctuation-induced and built-in anomalous scaling behaviour is reviewed and discussed.

  3. Rapid change of field line connectivity and reconnection in stochastic magnetic fields

    International Nuclear Information System (INIS)

    Huang, Yi-Min; Bhattacharjee, A.; Boozer, Allen H.

    2014-01-01

    Magnetic fields without a direction of continuous symmetry have the generic feature that neighboring field lines exponentiate away from each other and become stochastic, and hence the ideal constraint of preserving magnetic field line connectivity becomes exponentially sensitive to small deviations from ideal Ohm's law. The idea of breaking field line connectivity by stochasticity as a mechanism for fast reconnection is tested with numerical simulations based on reduced magnetohydrodynamics equations with a strong guide field line-tied to two perfectly conducting end plates. Starting from an ideally stable force-free equilibrium, the system is allowed to undergo resistive relaxation. Two distinct phases are found in the process of resistive relaxation. During the quasi-static phase, rapid change of field line connectivity and strong induced flow are found in regions of high field line exponentiation. However, although the field line connectivity of individual field lines can change rapidly, the overall pattern of field line mapping appears to deform gradually. From this perspective, field line exponentiation appears to cause enhanced diffusion rather than reconnection. In some cases, resistive quasi-static evolution can cause the ideally stable initial equilibrium to cross a stability threshold, leading to formation of intense current filaments and rapid change of field line mapping into a qualitatively different pattern. It is in this onset phase that the change of field line connectivity is more appropriately designated as magnetic reconnection. Our results show that rapid change of field line connectivity appears to be a necessary, but not a sufficient condition for fast reconnection.

  4. Dynamic phase transitions of the Blume–Emery–Griffiths model under an oscillating external magnetic field by the path probability method

    Energy Technology Data Exchange (ETDEWEB)

    Ertaş, Mehmet, E-mail: mehmetertas@erciyes.edu.tr; Keskin, Mustafa

    2015-03-01

    By using the path probability method (PPM) with point distribution, we study the dynamic phase transitions (DPTs) in the Blume–Emery–Griffiths (BEG) model under an oscillating external magnetic field. The phases in the model are obtained by solving the dynamic equations for the average order parameters and a disordered phase, ordered phase and four mixed phases are found. We also investigate the thermal behavior of the dynamic order parameters to analyze the nature dynamic transitions as well as to obtain the DPT temperatures. The dynamic phase diagrams are presented in three different planes in which exhibit the dynamic tricritical point, double critical end point, critical end point, quadrupole point, triple point as well as the reentrant behavior, strongly depending on the values of the system parameters. We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • Dynamic magnetic behavior of the Blume–Emery–Griffiths system is investigated by using the path probability method. • The time variations of average magnetizations are studied to find the phases. • The temperature dependence of the dynamic magnetizations is investigated to obtain the dynamic phase transition points. • We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory.

  5. Dynamic phase transitions of the Blume–Emery–Griffiths model under an oscillating external magnetic field by the path probability method

    International Nuclear Information System (INIS)

    Ertaş, Mehmet; Keskin, Mustafa

    2015-01-01

    By using the path probability method (PPM) with point distribution, we study the dynamic phase transitions (DPTs) in the Blume–Emery–Griffiths (BEG) model under an oscillating external magnetic field. The phases in the model are obtained by solving the dynamic equations for the average order parameters and a disordered phase, ordered phase and four mixed phases are found. We also investigate the thermal behavior of the dynamic order parameters to analyze the nature dynamic transitions as well as to obtain the DPT temperatures. The dynamic phase diagrams are presented in three different planes in which exhibit the dynamic tricritical point, double critical end point, critical end point, quadrupole point, triple point as well as the reentrant behavior, strongly depending on the values of the system parameters. We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • Dynamic magnetic behavior of the Blume–Emery–Griffiths system is investigated by using the path probability method. • The time variations of average magnetizations are studied to find the phases. • The temperature dependence of the dynamic magnetizations is investigated to obtain the dynamic phase transition points. • We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory

  6. Stochastic pump effect and geometric phases in dissipative and stochastic systems

    Energy Technology Data Exchange (ETDEWEB)

    Sinitsyn, Nikolai [Los Alamos National Laboratory

    2008-01-01

    The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).

  7. Field theory of absorbing phase transitions with a non-diffusive conserved field

    International Nuclear Information System (INIS)

    Pastor-Satorras, R.; Vespignani, A.

    2000-04-01

    We investigate the critical behavior of a reaction-diffusion system exhibiting a continuous absorbing-state phase transition. The reaction-diffusion system strictly conserves the total density of particles, represented as a non-diffusive conserved field, and allows an infinite number of absorbing configurations. Numerical results show that it belongs to a wide universality class that also includes stochastic sandpile models. We derive microscopically the field theory representing this universality class. (author)

  8. Phase Field Modeling Using PetIGA

    KAUST Repository

    Vignal, Philippe

    2013-06-01

    Phase field modeling has become a widely used framework in the computational material science community. Its ability to model different problems by defining appropriate phase field parameters and relating it to a free energy functional makes it highly versatile. Thermodynamically consistent partial differential equations can then be generated by assuming dissipative dynamics, and setting up the problem as one of minimizing this free energy. The equations are nonetheless challenging to solve, and having a highly efficient and parallel framework to solve them is necessary. In this work, a brief review on phase field models is given, followed by a short analysis of the Phase Field Crystal Model solved with Isogeometric Analysis us- ing PetIGA. We end with an introduction to a new modeling concept, where free energy functions are built with a periodic equilibrium structure in mind.

  9. Anisotropy in wavelet-based phase field models

    KAUST Repository

    Korzec, Maciek; Mü nch, Andreas; Sü li, Endre; Wagner, Barbara

    2016-01-01

    When describing the anisotropic evolution of microstructures in solids using phase-field models, the anisotropy of the crystalline phases is usually introduced into the interfacial energy by directional dependencies of the gradient energy coefficients. We consider an alternative approach based on a wavelet analogue of the Laplace operator that is intrinsically anisotropic and linear. The paper focuses on the classical coupled temperature/Ginzburg--Landau type phase-field model for dendritic growth. For the model based on the wavelet analogue, existence, uniqueness and continuous dependence on initial data are proved for weak solutions. Numerical studies of the wavelet based phase-field model show dendritic growth similar to the results obtained for classical phase-field models.

  10. Anisotropy in wavelet-based phase field models

    KAUST Repository

    Korzec, Maciek

    2016-04-01

    When describing the anisotropic evolution of microstructures in solids using phase-field models, the anisotropy of the crystalline phases is usually introduced into the interfacial energy by directional dependencies of the gradient energy coefficients. We consider an alternative approach based on a wavelet analogue of the Laplace operator that is intrinsically anisotropic and linear. The paper focuses on the classical coupled temperature/Ginzburg--Landau type phase-field model for dendritic growth. For the model based on the wavelet analogue, existence, uniqueness and continuous dependence on initial data are proved for weak solutions. Numerical studies of the wavelet based phase-field model show dendritic growth similar to the results obtained for classical phase-field models.

  11. Full particle orbit effects in regular and stochastic magnetic fields

    Energy Technology Data Exchange (ETDEWEB)

    Ogawa, Shun, E-mail: shun.ogawa@cpt.univ-mrs.fr [Aix Marseille Univ., Univ. Toulon, CNRS, CPT, Marseille (France); CEA, IRFM, F-13108 St. Paul-lez-Durance Cedex (France); Cambon, Benjamin; Leoncini, Xavier; Vittot, Michel [Aix Marseille Univ., Univ. Toulon, CNRS, CPT, Marseille (France); Castillo-Negrete, Diego del [Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6169 (United States); Dif-Pradalier, Guilhem; Garbet, Xavier [CEA, IRFM, F-13108 St. Paul-lez-Durance Cedex (France)

    2016-07-15

    We present a numerical study of charged particle motion in a time-independent magnetic field in cylindrical geometry. The magnetic field model consists of an unperturbed reversed-shear (non-monotonic q-profile) helical part and a perturbation consisting of a superposition of modes. Contrary to most of the previous studies, the particle trajectories are computed by directly solving the full Lorentz force equations of motion in a six-dimensional phase space using a sixth-order, implicit, symplectic Gauss-Legendre method. The level of stochasticity in the particle orbits is diagnosed using averaged, effective Poincare sections. It is shown that when only one mode is present, the particle orbits can be stochastic even though the magnetic field line orbits are not stochastic (i.e., fully integrable). The lack of integrability of the particle orbits in this case is related to separatrix crossing and the breakdown of the global conservation of the magnetic moment. Some perturbation consisting of two modes creates resonance overlapping, leading to Hamiltonian chaos in magnetic field lines. Then, the particle orbits exhibit a nontrivial dynamics depending on their energy and pitch angle. It is shown that the regions where the particle motion is stochastic decrease as the energy increases. The non-monotonicity of the q-profile implies the existence of magnetic ITBs (internal transport barriers) which correspond to shearless flux surfaces located in the vicinity of the q-profile minimum. It is shown that depending on the energy, these magnetic ITBs might or might not confine particles. That is, magnetic ITBs act as an energy-dependent particle confinement filter. Magnetic field lines in reversed-shear configurations exhibit topological bifurcations (from homoclinic to heteroclinic) due to separatrix reconnection. We show that a similar but more complex scenario appears in the case of particle orbits that depend in a non-trivial way on the energy and pitch angle of the

  12. Boundary induced phase transition with stochastic entrance and exit

    International Nuclear Information System (INIS)

    Mitra, Mithun Kumar; Chatterjee, Sakuntala

    2014-01-01

    We study an open-chain totally asymmetric exclusion process (TASEP) with stochastic gates present at the two boundaries. The gating dynamics has been modeled with the physical system of ion-channel gating in mind. These gates can randomly switch between an open state and a closed state. In the open state, the gates are highly permeable such that any particle arriving at the gate immediately passes through. In the closed state, a particle becomes trapped at the gate and cannot pass through until the gate switches open again. We calculate the phase-diagram of the system and find important and non-trivial differences with the phase-diagram of a regular open-chain TASEP. In particular, depending on the switching rates of the two gates, the system may or may not admit a maximal current phase. Our analytic calculations within mean-field theory capture the main qualitative features of our Monte Carlo simulation results. We also perform a refined mean-field calculation where the correlations at the boundaries are taken into account. This theory shows significantly better quantitative agreement with our simulation results. (paper)

  13. Universal phase transition in community detectability under a stochastic block model.

    Science.gov (United States)

    Chen, Pin-Yu; Hero, Alfred O

    2015-03-01

    We prove the existence of an asymptotic phase-transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. Natl. Acad. Sci. (USA) 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the intercommunity edge connection probability p grows. This phase transition separates a subcritical regime of small p, where modularity-based community detection successfully identifies the communities, from a supercritical regime of large p where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase-transition threshold p* is equal to √[p1p2], where pi(i=1,2) is the within-community edge connection probability. Thus the phase-transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase-transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase-transition threshold, we propose an empirical method for estimating this threshold from real-world data.

  14. Phase Field Modeling Using PetIGA

    KAUST Repository

    Vignal, Philippe; Collier, Nathan; Calo, Victor M.

    2013-01-01

    , and having a highly efficient and parallel framework to solve them is necessary. In this work, a brief review on phase field models is given, followed by a short analysis of the Phase Field Crystal Model solved with Isogeometric Analysis us- ing PetIGA. We

  15. Stochasticity Modeling in Memristors

    KAUST Repository

    Naous, Rawan

    2015-10-26

    Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital feature for the integration of this nonlinear device into the stochastic electronics realm of study. In this paper, experimentally observed innate stochasticity is modeled in a circuit compatible format. The model proposed is generic and could be incorporated into variants of threshold-based memristor models in which apparent variations in the output hysteresis convey the switching threshold shift. Further application as a noise injection alternative paves the way for novel approaches in the fields of neuromorphic engineering circuits design. On the other hand, extra caution needs to be paid to variability intolerant digital designs based on non-deterministic memristor logic.

  16. Stochasticity Modeling in Memristors

    KAUST Repository

    Naous, Rawan; Al-Shedivat, Maruan; Salama, Khaled N.

    2015-01-01

    Diverse models have been proposed over the past years to explain the exhibiting behavior of memristors, the fourth fundamental circuit element. The models varied in complexity ranging from a description of physical mechanisms to a more generalized mathematical modeling. Nonetheless, stochasticity, a widespread observed phenomenon, has been immensely overlooked from the modeling perspective. This inherent variability within the operation of the memristor is a vital feature for the integration of this nonlinear device into the stochastic electronics realm of study. In this paper, experimentally observed innate stochasticity is modeled in a circuit compatible format. The model proposed is generic and could be incorporated into variants of threshold-based memristor models in which apparent variations in the output hysteresis convey the switching threshold shift. Further application as a noise injection alternative paves the way for novel approaches in the fields of neuromorphic engineering circuits design. On the other hand, extra caution needs to be paid to variability intolerant digital designs based on non-deterministic memristor logic.

  17. Hybrid approaches for multiple-species stochastic reaction–diffusion models

    International Nuclear Information System (INIS)

    Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K.; Byrne, Helen

    2015-01-01

    Reaction–diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction–diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model. - Highlights: • A novel hybrid stochastic/deterministic reaction–diffusion simulation method is given. • Can massively speed up stochastic simulations while preserving stochastic effects. • Can handle multiple reacting species. • Can handle moving boundaries

  18. Hybrid approaches for multiple-species stochastic reaction–diffusion models

    Energy Technology Data Exchange (ETDEWEB)

    Spill, Fabian, E-mail: fspill@bu.edu [Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States); Guerrero, Pilar [Department of Mathematics, University College London, Gower Street, London WC1E 6BT (United Kingdom); Alarcon, Tomas [Centre de Recerca Matematica, Campus de Bellaterra, Edifici C, 08193 Bellaterra (Barcelona) (Spain); Departament de Matemàtiques, Universitat Atonòma de Barcelona, 08193 Bellaterra (Barcelona) (Spain); Maini, Philip K. [Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); Byrne, Helen [Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); Computational Biology Group, Department of Computer Science, University of Oxford, Oxford OX1 3QD (United Kingdom)

    2015-10-15

    Reaction–diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction–diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model. - Highlights: • A novel hybrid stochastic/deterministic reaction–diffusion simulation method is given. • Can massively speed up stochastic simulations while preserving stochastic effects. • Can handle multiple reacting species. • Can handle moving boundaries.

  19. Modelling and application of stochastic processes

    CERN Document Server

    1986-01-01

    The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realiza­ tion problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically ef­ ficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side,...

  20. Modelling of diesel spray flame under engine-like conditions using an accelerated eulerian stochastic fields method: A convergence study of the number of stochastic fields

    OpenAIRE

    Pang, Kar Mun; Jangi, Mehdi; Bai, X.-S.; Schramm, Jesper; Walther, Jens Honore

    2016-01-01

    The use of transported Probability Density Function(PDF) methods allows a single model to compute the autoignition, premixed mode and diffusion flame of diesel combustion under engine-like conditions [1,2]. The Lagrangian particle based transported PDF models have been validated across a wide range of conditions [2,3]. Alternatively, the transported PDF model can also be formulated in the Eulerian framework[4]. The Eulerian PDF is commonly known as the Eulerian Stochastic Fields (ESF) model. ...

  1. Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models

    Science.gov (United States)

    Butler, T.; Graham, L.; Estep, D.; Dawson, C.; Westerink, J. J.

    2015-04-01

    The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

  2. Multiple fields in stochastic inflation

    Energy Technology Data Exchange (ETDEWEB)

    Assadullahi, Hooshyar [Institute of Cosmology & Gravitation, University of Portsmouth,Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX (United Kingdom); Firouzjahi, Hassan [School of Astronomy, Institute for Research in Fundamental Sciences (IPM),P.O. Box 19395-5531, Tehran (Iran, Islamic Republic of); Noorbala, Mahdiyar [Department of Physics, University of Tehran,P.O. Box 14395-547, Tehran (Iran, Islamic Republic of); School of Astronomy, Institute for Research in Fundamental Sciences (IPM),P.O. Box 19395-5531, Tehran (Iran, Islamic Republic of); Vennin, Vincent; Wands, David [Institute of Cosmology & Gravitation, University of Portsmouth,Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX (United Kingdom)

    2016-06-24

    Stochastic effects in multi-field inflationary scenarios are investigated. A hierarchy of diffusion equations is derived, the solutions of which yield moments of the numbers of inflationary e-folds. Solving the resulting partial differential equations in multi-dimensional field space is more challenging than the single-field case. A few tractable examples are discussed, which show that the number of fields is, in general, a critical parameter. When more than two fields are present for instance, the probability to explore arbitrarily large-field regions of the potential, otherwise inaccessible to single-field dynamics, becomes non-zero. In some configurations, this gives rise to an infinite mean number of e-folds, regardless of the initial conditions. Another difference with respect to single-field scenarios is that multi-field stochastic effects can be large even at sub-Planckian energy. This opens interesting new possibilities for probing quantum effects in inflationary dynamics, since the moments of the numbers of e-folds can be used to calculate the distribution of primordial density perturbations in the stochastic-δN formalism.

  3. Lateral phase drift of the topological charge density in stochastic optical fields

    CSIR Research Space (South Africa)

    Roux, FS

    2012-03-01

    Full Text Available The statistical distributions of optical vortices or topological charge in stochastic optical fields can be inhomogeneous in both transverse directions. Such two-dimensional inhomogeneous vortex or topological charge distributions evolve in a...

  4. A phase field model for segregation and precipitation induced by irradiation in alloys

    Science.gov (United States)

    Badillo, A.; Bellon, P.; Averback, R. S.

    2015-04-01

    A phase field model is introduced to model the evolution of multicomponent alloys under irradiation, including radiation-induced segregation and precipitation. The thermodynamic and kinetic components of this model are derived using a mean-field model. The mobility coefficient and the contribution of chemical heterogeneity to free energy are rescaled by the cell size used in the phase field model, yielding microstructural evolutions that are independent of the cell size. A new treatment is proposed for point defect clusters, using a mixed discrete-continuous approach to capture the stochastic character of defect cluster production in displacement cascades, while retaining the efficient modeling of the fate of these clusters using diffusion equations. The model is tested on unary and binary alloy systems using two-dimensional simulations. In a unary system, the evolution of point defects under irradiation is studied in the presence of defect clusters, either pre-existing ones or those created by irradiation, and compared with rate theory calculations. Binary alloys with zero and positive heats of mixing are then studied to investigate the effect of point defect clustering on radiation-induced segregation and precipitation in undersaturated solid solutions. Lastly, irradiation conditions and alloy parameters leading to irradiation-induced homogeneous precipitation are investigated. The results are discussed in the context of experimental results reported for Ni-Si and Al-Zn undersaturated solid solutions subjected to irradiation.

  5. A phase field model for segregation and precipitation induced by irradiation in alloys

    International Nuclear Information System (INIS)

    Badillo, A; Bellon, P; Averback, R S

    2015-01-01

    A phase field model is introduced to model the evolution of multicomponent alloys under irradiation, including radiation-induced segregation and precipitation. The thermodynamic and kinetic components of this model are derived using a mean-field model. The mobility coefficient and the contribution of chemical heterogeneity to free energy are rescaled by the cell size used in the phase field model, yielding microstructural evolutions that are independent of the cell size. A new treatment is proposed for point defect clusters, using a mixed discrete-continuous approach to capture the stochastic character of defect cluster production in displacement cascades, while retaining the efficient modeling of the fate of these clusters using diffusion equations. The model is tested on unary and binary alloy systems using two-dimensional simulations. In a unary system, the evolution of point defects under irradiation is studied in the presence of defect clusters, either pre-existing ones or those created by irradiation, and compared with rate theory calculations. Binary alloys with zero and positive heats of mixing are then studied to investigate the effect of point defect clustering on radiation-induced segregation and precipitation in undersaturated solid solutions. Lastly, irradiation conditions and alloy parameters leading to irradiation-induced homogeneous precipitation are investigated. The results are discussed in the context of experimental results reported for Ni–Si and Al–Zn undersaturated solid solutions subjected to irradiation. (paper)

  6. Dynamic phase transition in the kinetic spin-2 Blume-Emery-Griffiths model in an oscillating field

    International Nuclear Information System (INIS)

    Ertas, Mehmet; Canko, Osman; Keskin, Mustafa

    2008-01-01

    We extend our recent paper [M. Keskin, O. Canko, M. Ertas, J. Exp. Theor. Phys. (Sov. Phys. JETP) 105 (2007) 1190.] to present a study, within a mean-field approach, the stationary states of the kinetic spin-2 Blume-Emery-Griffiths model in the presence of a time-dependent oscillating magnetic field by using the Glauber-type of stochastic dynamics. We found 20 fundamental types of dynamic phase diagrams where exhibit more complex and richer phase diagrams than our recent paper. Especially, the obtained dynamic phase diagrams show the dynamic triple, quadruple and dynamic double critical end points besides dynamic tricritical points that depending on interaction parameters. The phase diagrams also exhibit a disordered (d) and the ferromagnetic-2 (f 2 ) phases, and the f 2 +d, f 2 +fq, fq+d, f 2 +f 1 +fq and f 2 +fq+d, where f 1 are fq the ferromagnetic-1 and ferroquadrupolar or simply quadrupolar phases respectively, coexistence phase regions that strongly depend on interaction parameters

  7. Dynamic phase transition in the kinetic spin-2 Blume-Emery-Griffiths model in an oscillating field

    Science.gov (United States)

    Ertaş, Mehmet; Canko, Osman; Keskin, Mustafa

    We extend our recent paper [M. Keskin, O. Canko, M. Ertaş, J. Exp. Theor. Phys. (Sov. Phys. JETP) 105 (2007) 1190.] to present a study, within a mean-field approach, the stationary states of the kinetic spin-2 Blume-Emery-Griffiths model in the presence of a time-dependent oscillating magnetic field by using the Glauber-type of stochastic dynamics. We found 20 fundamental types of dynamic phase diagrams where exhibit more complex and richer phase diagrams than our recent paper. Especially, the obtained dynamic phase diagrams show the dynamic triple, quadruple and dynamic double critical end points besides dynamic tricritical points that depending on interaction parameters. The phase diagrams also exhibit a disordered ( d) and the ferromagnetic-2 ( f2) phases, and the f2+ d, f2+ fq, fq+ d, f2+ f1+ fq and f2+ fq+ d, where f1 are fq the ferromagnetic-1 and ferroquadrupolar or simply quadrupolar phases respectively, coexistence phase regions that strongly depend on interaction parameters.

  8. Can stochastic consumer phase models in QMRA be simplified to a single factor?

    DEFF Research Database (Denmark)

    Neves, Maria Ines; Mungai, Sylvester N.; Nauta, Maarten J.

    2018-01-01

    , and a consumer phase model (CPM) needs to be included in a QMRA to allow an evaluation of the effectiveness of intervention measures in food production and processing in terms of human health risk. However, the development of a CPM is complex because consumer practices can be highly variable and data are scarce......In quantitative microbiological risk assessment (QMRA), the consumer phase covers the part of the food chain following production and retail, where the consumer transports, stores, prepares and consumes the food products considered. These consumer practices have a crucial impact on exposure......-implemented and their equivalent surrogate models were derived, basing the value of the constant surrogate model factor on the absolute risk estimate from the stochastic model. The performances of the models were evaluated by comparing the effects of hypothetical intervention measures that reduce the mean or the standard...

  9. Hybrid approaches for multiple-species stochastic reaction-diffusion models

    Science.gov (United States)

    Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K.; Byrne, Helen

    2015-10-01

    Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

  10. Hybrid approaches for multiple-species stochastic reaction-diffusion models.

    KAUST Repository

    Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K; Byrne, Helen

    2015-01-01

    Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

  11. Hybrid approaches for multiple-species stochastic reaction-diffusion models.

    KAUST Repository

    Spill, Fabian

    2015-10-01

    Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

  12. Some stochastic techniques in quantization, new developments in Markov fields and quantum fields

    International Nuclear Information System (INIS)

    Albeverio, S.; Zegarlinski, B.

    1990-01-01

    In these lectures we intend to discuss a few recent developments in the area of interactions between quantum fields and Markow fields in which we have been involved. We stress particularly developments involving techniques of stochastic analysis and where mathematical results have been obtained. In sections 1 and 2 we discuss recent developments in the study and applications of the theory of Dirichlet forms in its relations with quantum mechanics and quantum field theory. In our opinion, this theory provides a natural setting for the study of the singular stochastic processes associated with quantum theory. In section 3 we discuss a recent rigorous construction of a convergent simplicial approximation to quantum fields. We look upon these developments as a first step towards a mathematical realization, at least in 2 space-time dimensions, of a convergent 'Regge-calculus', and as first steps to the mathematical control of more general models (like e.g. models involving actions of Chern-Simons type) in the continuum. In Sect. 4 we discuss applications of some stochastic techniques to the study of gauge fields and Higgs fields, mainly in 2 space time dimensions and certain non linear electromagnetic-type fields in 4-space-time dimensions. (orig./HSI)

  13. High-performance phase-field modeling

    KAUST Repository

    Vignal, Philippe

    2015-04-27

    Many processes in engineering and sciences involve the evolution of interfaces. Among the mathematical frameworks developed to model these types of problems, the phase-field method has emerged as a possible solution. Phase-fields nonetheless lead to complex nonlinear, high-order partial differential equations, whose solution poses mathematical and computational challenges. Guaranteeing some of the physical properties of the equations has lead to the development of efficient algorithms and discretizations capable of recovering said properties by construction [2, 5]. This work builds-up on these ideas, and proposes novel discretization strategies that guarantee numerical energy dissipation for both conserved and non-conserved phase-field models. The temporal discretization is based on a novel method which relies on Taylor series and ensures strong energy stability. It is second-order accurate, and can also be rendered linear to speed-up the solution process [4]. The spatial discretization relies on Isogeometric Analysis, a finite element method that possesses the k-refinement technology and enables the generation of high-order, high-continuity basis functions. These basis functions are well suited to handle the high-order operators present in phase-field models. Two-dimensional and three dimensional results of the Allen-Cahn, Cahn-Hilliard, Swift-Hohenberg and phase-field crystal equation will be presented, which corroborate the theoretical findings, and illustrate the robustness of the method. Results related to more challenging examples, namely the Navier-Stokes Cahn-Hilliard and a diusion-reaction Cahn-Hilliard system, will also be presented. The implementation was done in PetIGA and PetIGA-MF, high-performance Isogeometric Analysis frameworks [1, 3], designed to handle non-linear, time-dependent problems.

  14. Dynamic phase transition in the kinetic spin-3/2 Blume-Emery-Griffiths model in an oscillating field

    Energy Technology Data Exchange (ETDEWEB)

    Canko, Osman; Deviren, Bayram; Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

    2006-07-26

    The dynamic phase transitions are studied, within a mean-field approach, in the kinetic Blume-Emery-Griffiths model under the presence of a time varying (sinusoidal) magnetic field by using the Glauber-type stochastic dynamics. The behaviour of the time-dependence of the order parameters and the behaviour of the average order parameters in a period, which is also called the dynamic order parameters, as a function of reduced temperature, are investigated. The nature (continuous and discontinuous) of transition is characterized by studying the average order parameters in a period. The dynamic phase transition points are obtained and the phase diagrams are presented in the reduced magnetic field amplitude and reduced temperature plane. The phase diagrams exhibit one, two, or three dynamic tricritical points and a dynamic double critical end point, and besides a disordered and two ordered phases, seven coexistence phase regions exist, which strongly depend on interaction parameters. We also calculate the Liapunov exponent to verify the stability of solutions and the dynamic phase transition points.

  15. Dynamic phase transition in the kinetic spin-3/2 Blume-Emery-Griffiths model in an oscillating field

    International Nuclear Information System (INIS)

    Canko, Osman; Deviren, Bayram; Keskin, Mustafa

    2006-01-01

    The dynamic phase transitions are studied, within a mean-field approach, in the kinetic Blume-Emery-Griffiths model under the presence of a time varying (sinusoidal) magnetic field by using the Glauber-type stochastic dynamics. The behaviour of the time-dependence of the order parameters and the behaviour of the average order parameters in a period, which is also called the dynamic order parameters, as a function of reduced temperature, are investigated. The nature (continuous and discontinuous) of transition is characterized by studying the average order parameters in a period. The dynamic phase transition points are obtained and the phase diagrams are presented in the reduced magnetic field amplitude and reduced temperature plane. The phase diagrams exhibit one, two, or three dynamic tricritical points and a dynamic double critical end point, and besides a disordered and two ordered phases, seven coexistence phase regions exist, which strongly depend on interaction parameters. We also calculate the Liapunov exponent to verify the stability of solutions and the dynamic phase transition points

  16. Diffusion with intrinsic trapping in 2-d incompressible stochastic velocity fields

    International Nuclear Information System (INIS)

    Vlad, M.; Spineanu, F.; Misguich, J.H.; Vlad, M.; Spineanu, F.; Balescu, R.

    1998-10-01

    A new statistical approach that applies to the high Kubo number regimes for particle diffusion in stochastic velocity fields is presented. This 2-dimensional model describes the partial trapping of the particles in the stochastic field. the results are close to the numerical simulations and also to the estimations based on percolation theory. (authors)

  17. The effects of noise on binocular rivalry waves: a stochastic neural field model

    International Nuclear Information System (INIS)

    Webber, Matthew A; Bressloff, Paul C

    2013-01-01

    We analyze the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction–diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. We use our analysis to calculate the first-passage-time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation lead to quenched disorder in the neural fields during propagation of a wave. (paper)

  18. Phase-field model of eutectic growth

    International Nuclear Information System (INIS)

    Karma, A.

    1994-01-01

    A phase-field model which describes the solidification of a binary eutectic alloy with a simple symmetric phase diagram is introduced and the sharp-interface limit of this model is explored both analytically and numerically

  19. Stochastic lattice model of synaptic membrane protein domains.

    Science.gov (United States)

    Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A

    2017-05-01

    Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.

  20. Stochastic models of cell motility

    DEFF Research Database (Denmark)

    Gradinaru, Cristian

    2012-01-01

    Cell motility and migration are central to the development and maintenance of multicellular organisms, and errors during this process can lead to major diseases. Consequently, the mechanisms and phenomenology of cell motility are currently under intense study. In recent years, a new...... interdisciplinary field focusing on the study of biological processes at the nanoscale level, with a range of technological applications in medicine and biological research, has emerged. The work presented in this thesis is at the interface of cell biology, image processing, and stochastic modeling. The stochastic...... models introduced here are based on persistent random motion, which I apply to real-life studies of cell motility on flat and nanostructured surfaces. These models aim to predict the time-dependent position of cell centroids in a stochastic manner, and conversely determine directly from experimental...

  1. Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model

    Directory of Open Access Journals (Sweden)

    Michael Schultz

    2018-03-01

    Full Text Available Efficient boarding procedures have to consider both operational constraints and the individual passenger behavior. In contrast to the aircraft handling processes of fueling, catering and cleaning, the boarding process is more driven by passengers than by airport or airline operators. This paper delivers a comprehensive set of operational data including classification of boarding times, passenger arrival times, times to store hand luggage, and passenger interactions in the aircraft cabin as a reliable basis for calibrating models for aircraft boarding. In this paper, a microscopic approach is used to model the passenger behavior, where the passenger movement is defined as a one-dimensional, stochastic, and time/space discrete transition process. This model is used to compare measurements from field trials of boarding procedures with simulation results and demonstrates a deviation smaller than 5%.

  2. The phase field technique for modeling multiphase materials

    Science.gov (United States)

    Singer-Loginova, I.; Singer, H. M.

    2008-10-01

    This paper reviews methods and applications of the phase field technique, one of the fastest growing areas in computational materials science. The phase field method is used as a theory and computational tool for predictions of the evolution of arbitrarily shaped morphologies and complex microstructures in materials. In this method, the interface between two phases (e.g. solid and liquid) is treated as a region of finite width having a gradual variation of different physical quantities, i.e. it is a diffuse interface model. An auxiliary variable, the phase field or order parameter \\phi(\\vec{x}) , is introduced, which distinguishes one phase from the other. Interfaces are identified by the variation of the phase field. We begin with presenting the physical background of the phase field method and give a detailed thermodynamical derivation of the phase field equations. We demonstrate how equilibrium and non-equilibrium physical phenomena at the phase interface are incorporated into the phase field methods. Then we address in detail dendritic and directional solidification of pure and multicomponent alloys, effects of natural convection and forced flow, grain growth, nucleation, solid-solid phase transformation and highlight other applications of the phase field methods. In particular, we review the novel phase field crystal model, which combines atomistic length scales with diffusive time scales. We also discuss aspects of quantitative phase field modeling such as thin interface asymptotic analysis and coupling to thermodynamic databases. The phase field methods result in a set of partial differential equations, whose solutions require time-consuming large-scale computations and often limit the applicability of the method. Subsequently, we review numerical approaches to solve the phase field equations and present a finite difference discretization of the anisotropic Laplacian operator.

  3. Dynamic phase transition in the kinetic spin-2 Blume-Emery-Griffiths model in an oscillating field

    Energy Technology Data Exchange (ETDEWEB)

    Ertas, Mehmet [Institute of Science, Erciyes University, 38039 Kayseri (Turkey); Canko, Osman [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)], E-mail: keskin@erciyes.edu.tr

    2008-06-15

    We extend our recent paper [M. Keskin, O. Canko, M. Ertas, J. Exp. Theor. Phys. (Sov. Phys. JETP) 105 (2007) 1190.] to present a study, within a mean-field approach, the stationary states of the kinetic spin-2 Blume-Emery-Griffiths model in the presence of a time-dependent oscillating magnetic field by using the Glauber-type of stochastic dynamics. We found 20 fundamental types of dynamic phase diagrams where exhibit more complex and richer phase diagrams than our recent paper. Especially, the obtained dynamic phase diagrams show the dynamic triple, quadruple and dynamic double critical end points besides dynamic tricritical points that depending on interaction parameters. The phase diagrams also exhibit a disordered (d) and the ferromagnetic-2 (f{sub 2}) phases, and the f{sub 2}+d, f{sub 2}+fq, fq+d, f{sub 2}+f{sub 1}+fq and f{sub 2}+fq+d, where f{sub 1} are fq the ferromagnetic-1 and ferroquadrupolar or simply quadrupolar phases respectively, coexistence phase regions that strongly depend on interaction parameters.

  4. Stochastic quantization of gravity and string fields

    International Nuclear Information System (INIS)

    Rumpf, H.

    1986-01-01

    The stochastic quantization method of Parisi and Wu is generalized so as to make it applicable to Einstein's theory of gravitation. The generalization is based on the existence of a preferred metric in field configuration space, involves Ito's calculus, and introduces a complex stochastic process adapted to Lorentzian spacetime. It implies formally the path integral measure of DeWitt, a causual Feynman propagator, and a consistent stochastic perturbation theory. The lineraized version of the theory is also obtained from the stochastic quantization of the free string field theory of Siegel and Zwiebach. (Author)

  5. Analysis of a Stochastic Chemical System Close to a SNIPER Bifurcation of Its Mean-Field Model

    KAUST Repository

    Erban, Radek; Chapman, S. Jonathan; Kevrekidis, Ioannis G.; Vejchodský , Tomá š

    2009-01-01

    A framework for the analysis of stochastic models of chemical systems for which the deterministic mean-field description is undergoing a saddle-node infinite period (SNIPER) bifurcation is presented. Such a bifurcation occurs, for example

  6. COMPARISON OF EIGENMODE-BASED AND RANDOM FIELD-BASED IMPERFECTION MODELING FOR THE STOCHASTIC BUCKLING ANALYSIS OF I-SECTION BEAM–COLUMNS

    KAUST Repository

    STAVREV, A.

    2013-03-01

    The uncertainty of geometric imperfections in a series of nominally equal I-beams leads to a variability of corresponding buckling loads. Its analysis requires a stochastic imperfection model, which can be derived either by the simple variation of the critical eigenmode with a scalar random variable, or with the help of the more advanced theory of random fields. The present paper first provides a concise review of the two different modeling approaches, covering theoretical background, assumptions and calibration, and illustrates their integration into commercial finite element software to conduct stochastic buckling analyses with the Monte-Carlo method. The stochastic buckling behavior of an example beam is then simulated with both stochastic models, calibrated from corresponding imperfection measurements. The simulation results show that for different load cases, the response statistics of the buckling load obtained with the eigenmode-based and the random field-based models agree very well. A comparison of our simulation results with corresponding Eurocode 3 limit loads indicates that the design standard is very conservative for compression dominated load cases. © 2013 World Scientific Publishing Company.

  7. The effects of noise on binocular rivalry waves: a stochastic neural field model

    KAUST Repository

    Webber, Matthew A

    2013-03-12

    We analyze the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. We use our analysis to calculate the first-passage-time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation lead to quenched disorder in the neural fields during propagation of a wave. © 2013 IOP Publishing Ltd and SISSA Medialab srl.

  8. Dynamic phase transitions and dynamic phase diagrams in the kinetic spin-5/2 Blume–Capel model in an oscillating external magnetic field: Effective-field theory and the Glauber-type stochastic dynamics approach

    International Nuclear Information System (INIS)

    Ertaş, Mehmet; Keskin, Mustafa; Deviren, Bayram

    2012-01-01

    Using an effective field theory with correlations, we study a kinetic spin-5/2 Blume–Capel model with bilinear exchange interaction and single-ion crystal field on a square lattice. The effective-field dynamic equation is derived by employing the Glauber transition rates. First, the phases in the kinetic system are obtained by solving this dynamic equation. Then, the thermal behavior of the dynamic magnetization, the hysteresis loop area and correlation are investigated in order to characterize the nature of the dynamic transitions and to obtain dynamic phase transition temperatures. Finally, we present the phase diagrams in two planes, namely (T/zJ, h 0 /zJ) and (T/zJ, D/zJ), where T absolute temperature, h 0 , the amplitude of the oscillating field, D, crystal field interaction or single-ion anisotropy constant and z denotes the nearest-neighbor sites of the central site. The phase diagrams exhibit four fundamental phases and ten mixed phases which are composed of binary, ternary and tetrad combination of fundamental phases, depending on the crystal field interaction parameter. Moreover, the phase diagrams contain a dynamic tricritical point (T), a double critical end point (B), a multicritical point (A) and zero-temperature critical point (Z). - Highlights: ► The effective-field theory is used to study the kinetic spin-5/2 Ising Blume–Capel model. ► Time variations of average order parameter have been studied to find phases in the system. ► The dynamic magnetization, hysteresis loop area and correlation have been calculated. ► The dynamic phase boundaries of the system depend on D/zJ. ► The dynamic phase diagrams are presented in the (T/zJ, h 0 /zJ) and (D/zJ, T/zJ) planes.

  9. Dynamic phase transitions and dynamic phase diagrams in the kinetic spin-5/2 Blume-Capel model in an oscillating external magnetic field: Effective-field theory and the Glauber-type stochastic dynamics approach

    Energy Technology Data Exchange (ETDEWEB)

    Ertas, Mehmet [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Keskin, Mustafa, E-mail: keskin@erciyes.edu.tr [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Deviren, Bayram [Department of Physics, Nevsehir University, 50300 Nevsehir (Turkey)

    2012-04-15

    Using an effective field theory with correlations, we study a kinetic spin-5/2 Blume-Capel model with bilinear exchange interaction and single-ion crystal field on a square lattice. The effective-field dynamic equation is derived by employing the Glauber transition rates. First, the phases in the kinetic system are obtained by solving this dynamic equation. Then, the thermal behavior of the dynamic magnetization, the hysteresis loop area and correlation are investigated in order to characterize the nature of the dynamic transitions and to obtain dynamic phase transition temperatures. Finally, we present the phase diagrams in two planes, namely (T/zJ, h{sub 0}/zJ) and (T/zJ, D/zJ), where T absolute temperature, h{sub 0}, the amplitude of the oscillating field, D, crystal field interaction or single-ion anisotropy constant and z denotes the nearest-neighbor sites of the central site. The phase diagrams exhibit four fundamental phases and ten mixed phases which are composed of binary, ternary and tetrad combination of fundamental phases, depending on the crystal field interaction parameter. Moreover, the phase diagrams contain a dynamic tricritical point (T), a double critical end point (B), a multicritical point (A) and zero-temperature critical point (Z). - Highlights: Black-Right-Pointing-Pointer The effective-field theory is used to study the kinetic spin-5/2 Ising Blume-Capel model. Black-Right-Pointing-Pointer Time variations of average order parameter have been studied to find phases in the system. Black-Right-Pointing-Pointer The dynamic magnetization, hysteresis loop area and correlation have been calculated. Black-Right-Pointing-Pointer The dynamic phase boundaries of the system depend on D/zJ. Black-Right-Pointing-Pointer The dynamic phase diagrams are presented in the (T/zJ, h{sub 0}/zJ) and (D/zJ, T/zJ) planes.

  10. Stochastic Differential Equation-Based Flexible Software Reliability Growth Model

    Directory of Open Access Journals (Sweden)

    P. K. Kapur

    2009-01-01

    Full Text Available Several software reliability growth models (SRGMs have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE-based generalized Erlang model with logistic error detection function. The model is estimated and validated on real-life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP-based models.

  11. Stochastic kinetics of photoinduced phase transitions in spin-crossover solids

    Science.gov (United States)

    Gudyma, Iurii; Maksymov, Artur; Dimian, Mihai

    2013-10-01

    We study the stochastic macroscopic kinetics of photoinduced phase transitions in spin-crossover compounds assisted by white and colored Ornstein-Uhlenbeck noise. By using a phenomenological master equation obtained in the mean-field approach, the phase diagram is constructed based on the associated Lyapunov function. The stochastic behavior is then analyzed in the Langevin framework and the corresponding Fokker-Planck equations. Both additive and multiplicative and white and colored types of noise are considered and the stationary probability densities are found along with the noise-assisted light induced hysteretic loops. By using the Kramers formalism, we also focus our attention on the escape time problem in these noise perturbed systems. A detailed study of the relative escape time dependence on various noise characteristics is performed and the main features are compared for different types of noise.

  12. Analysis of optical near-field energy transfer by stochastic model unifying architectural dependencies

    Energy Technology Data Exchange (ETDEWEB)

    Naruse, Makoto, E-mail: naruse@nict.go.jp [Photonic Network Research Institute, National Institute of Information and Communications Technology, 4-2-1 Nukui-kita, Koganei, Tokyo 184-8795 (Japan); Nanophotonics Research Center, Graduate School of Engineering, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656 (Japan); Akahane, Kouichi; Yamamoto, Naokatsu [Photonic Network Research Institute, National Institute of Information and Communications Technology, 4-2-1 Nukui-kita, Koganei, Tokyo 184-8795 (Japan); Holmström, Petter [Laboratory of Photonics and Microwave Engineering, Royal Institute of Technology (KTH), SE-164 40 Kista (Sweden); Thylén, Lars [Laboratory of Photonics and Microwave Engineering, Royal Institute of Technology (KTH), SE-164 40 Kista (Sweden); Hewlett-Packard Laboratories, Palo Alto, California 94304 (United States); Huant, Serge [Institut Néel, CNRS and Université Joseph Fourier, 25 rue des Martyrs BP 166, 38042 Grenoble Cedex 9 (France); Ohtsu, Motoichi [Nanophotonics Research Center, Graduate School of Engineering, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656 (Japan); Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656 (Japan)

    2014-04-21

    We theoretically and experimentally demonstrate energy transfer mediated by optical near-field interactions in a multi-layer InAs quantum dot (QD) structure composed of a single layer of larger dots and N layers of smaller ones. We construct a stochastic model in which optical near-field interactions that follow a Yukawa potential, QD size fluctuations, and temperature-dependent energy level broadening are unified, enabling us to examine device-architecture-dependent energy transfer efficiencies. The model results are consistent with the experiments. This study provides an insight into optical energy transfer involving inherent disorders in materials and paves the way to systematic design principles of nanophotonic devices that will allow optimized performance and the realization of designated functions.

  13. Analysis of optical near-field energy transfer by stochastic model unifying architectural dependencies

    International Nuclear Information System (INIS)

    Naruse, Makoto; Akahane, Kouichi; Yamamoto, Naokatsu; Holmström, Petter; Thylén, Lars; Huant, Serge; Ohtsu, Motoichi

    2014-01-01

    We theoretically and experimentally demonstrate energy transfer mediated by optical near-field interactions in a multi-layer InAs quantum dot (QD) structure composed of a single layer of larger dots and N layers of smaller ones. We construct a stochastic model in which optical near-field interactions that follow a Yukawa potential, QD size fluctuations, and temperature-dependent energy level broadening are unified, enabling us to examine device-architecture-dependent energy transfer efficiencies. The model results are consistent with the experiments. This study provides an insight into optical energy transfer involving inherent disorders in materials and paves the way to systematic design principles of nanophotonic devices that will allow optimized performance and the realization of designated functions

  14. Population stochastic modelling (PSM)-An R package for mixed-effects models based on stochastic differential equations

    DEFF Research Database (Denmark)

    Klim, Søren; Mortensen, Stig Bousgaard; Kristensen, Niels Rode

    2009-01-01

    are often partly ignored in PK/PD modelling although violating the hypothesis for many standard statistical tests. This article presents a package for the statistical program R that is able to handle SDEs in a mixed-effects setting. The estimation method implemented is the FOCE1 approximation......The extension from ordinary to stochastic differential equations (SDEs) in pharmacokinetic and pharmacodynamic (PK/PD) modelling is an emerging field and has been motivated in a number of articles [N.R. Kristensen, H. Madsen, S.H. Ingwersen, Using stochastic differential equations for PK/PD model...... development, J. Pharmacokinet. Pharmacodyn. 32 (February(l)) (2005) 109-141; C.W. Tornoe, R.V Overgaard, H. Agerso, H.A. Nielsen, H. Madsen, E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations, Pharm. Res. 22 (August(8...

  15. Stochastic linear programming models, theory, and computation

    CERN Document Server

    Kall, Peter

    2011-01-01

    This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. … T...

  16. Polarization of the vacuum by a stochastic external field

    International Nuclear Information System (INIS)

    Krive, I.V.; Pastur, L.A.; Rozhavskii, A.S.

    1988-01-01

    The effect of disorder, realized in the form of a fluctuating extra mass term, on the bosonic vacuum and fermionic vacuum of models of quantum field theory is studied. A method is developed for calculating the mean effective potential in the stochastic external field. For a model of interacting scalar and fermion fields in (3+1)-dimensional space-time it is shown that random fluctuations of the mass lead to an increase of the equilibrium mean scalar field in the system

  17. Stochastic electron dynamics due to drift waves in a sheared magnetic field and other drift motion problems

    International Nuclear Information System (INIS)

    Robertson, J.A.

    1986-12-01

    Electron motion in a single electrostatic wave in a sheared magnetic field is shown to become stochastic in the presence of a second wave at an amplitude well below that obtained from the overlapping pendulum resonance approximation. The enhanced stochasticity occurs for low parallel velocity electrons for which the parallel trapping motion from eE/sub parallel//m interacts strongly with the E x B trapping motion due to the presence of magnetic shear. The guiding-center equations for single particle electron orbits in given fields are investigated using both analytical and numerical techniques. The model assumes a slab magnetic field geometry with shear and two electrostatic plane waves propagating at an angle with respect to each other. Collisions and the self-consistent effect of the electron motion upon the fields are ignored. The guiding-center motion in an inertial reference frame moving in phase with the two waves is given by a two degree-of-freedom, autonomous Hamiltonian system. The single wave particle motion may be reduced to a two parameter family of one degree-of-freedom Hamiltonians which bifurcate from a pendulum phase space to a topology with three chains of elliptic and hyperbolic fixed points separated in radius about the mode-rational surface. In the presence of a perturbing wave with a different helicity, electrons in the small parallel velocity regime become stochastic at an amplitude scaling as the fourth root of the wave potential. The results obtained for stochastic motion apply directly to the problem of electron diffusion in drift waves occurring in toroidal fusion confinement devices. The effect of an adiabatically changing radial electric field upon guiding-center orbits in tokamaks is also investigated. This perturbation causes a radial polarization drift of trapped particle tokamak orbits

  18. Phase stability analysis of liquid-liquid equilibrium with stochastic methods

    Directory of Open Access Journals (Sweden)

    G. Nagatani

    2008-09-01

    Full Text Available Minimization of Gibbs free energy using activity coefficient models and nonlinear equation solution techniques is commonly applied to phase stability problems. However, when conventional techniques, such as the Newton-Raphson method, are employed, serious convergence problems may arise. Due to the existence of multiple solutions, several problems can be found in modeling liquid-liquid equilibrium of multicomponent systems, which are highly dependent on the initial guess. In this work phase stability analysis of liquid-liquid equilibrium is investigated using the NRTL model. For this purpose, two distinct stochastic numerical algorithms are employed to minimize the tangent plane distance of Gibbs free energy: a subdivision algorithm that can find all roots of nonlinear equations for liquid-liquid stability analysis and the Simulated Annealing method. Results obtained in this work for the two stochastic algorithms are compared with those of the Interval Newton method from the literature. Several different binary and multicomponent systems from the literature were successfully investigated.

  19. Regularity of solutions of a phase field model

    KAUST Repository

    Amler, Thomas

    2013-01-01

    Phase field models are widely-used for modelling phase transition processes such as solidification, freezing or CO2 sequestration. In this paper, a phase field model proposed by G. Caginalp is considered. The existence and uniqueness of solutions are proved in the case of nonsmooth initial data. Continuity of solutions with respect to time is established. In particular, it is shown that the governing initial boundary value problem can be considered as a dynamical system. © 2013 International Press.

  20. Quantization of dynamical systems and stochastic control theory

    International Nuclear Information System (INIS)

    Guerra, F.; Morato, L.M.

    1982-09-01

    In the general framework of stochastic control theory we introduce a suitable form of stochastic action associated to the controlled process. Then a variational principle gives all main features of Nelson's stochastic mechanics. In particular we derive the expression of the current velocity field as the gradient of the phase action. Moreover the stochastic corrections to the Hamilton-Jacobi equation are in agreement with the quantum mechanical form of the Madelung fluid (equivalent to the Schroedinger equation). Therefore stochastic control theory can provide a very simple model simulating quantum mechanical behavior

  1. An improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions

    CERN Document Server

    Kraft, M

    2003-01-01

    We propose an improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions. By combining forward and reverse reaction rates, a significant gain in computational efficiency is achieved. Two modifications of modelling the temperature dependence (with and without conservation of enthalpy) are introduced and studied quantitatively. The algorithm is tested for the combustion of n-heptane, which is a reference fuel component for internal combustion engines. The convergence of the algorithm is studied by a series of numerical experiments and the computational cost of the stochastic algorithm is compared with the DAE code DASSL. If less accuracy is needed the stochastic algorithm is faster on short simulation time intervals. The new stochastic algorithm is significantly faster than the original direct simulation algorithm in all cases considered.

  2. Multicritical dynamical phase diagrams of the kinetic Blume-Emery-Griffiths model with repulsive biquadratic coupling in an oscillating field

    Energy Technology Data Exchange (ETDEWEB)

    Temizer, Umuet [Department of Physics, Bozok University, 66100 Yozgat (Turkey); Kantar, Ersin [Institute of Science, Erciyes University, 38039 Kayseri (Turkey); Keskin, Mustafa [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)], E-mail: keskin@erciyes.edu.tr; Canko, Osman [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

    2008-06-15

    We study, within a mean-field approach, the stationary states of the kinetic Blume-Emery-Griffiths model with repulsive biquadratic coupling under the presence of a time-varying (sinusoidal) magnetic field. We employ the Glauber-type stochastic dynamics to construct set of dynamic equations of motion. The behavior of the time dependence of the order parameters and the behavior of the average order parameters in a period, which is also called the dynamic order parameters, as functions of the reduced temperature are investigated. The dynamic phase transition points are calculated and phase diagrams are presented in the reduced magnetic field amplitude and reduced temperature plane. The dynamical transition from one regime to the other can be of first- or second order depending on the region in the phase diagram. According to the values of the crystal field interaction or single-ion anisotropy constant and biquadratic exchange constant, we find 20 fundamental types of phase diagrams which exhibit many dynamic critical points, such as tricritical points, zero-temperature critical points, double critical end points, critical end point, triple point and multicritical point. Moreover, besides a disordered and ordered phases, seven coexistence phase regions exist in the system.

  3. Magnetic field line diffusion at the onset of stochasticity

    International Nuclear Information System (INIS)

    Elsaesser, K.; Deeskow, P.

    1987-01-01

    The Hamiltonian equations of a particle in a random set of waves just above the stochasticity threshold are considered both theoretically and numerically. First we derive the diffusion coefficient and the autocorrelation time perturbatively without using the thermodynamic limit, and we discuss the relevance of the Hamiltonian problem for particle acceleration and magnetic field line flow. Then we integrate the equations for an ensemble of magnetic field lines numerically for a model problem and show the time evolution of moments and correlations. Twice above the threshold we observe diffusive behaviour from the beginning, but the diffusion coefficient includes also the non-resonant modes. Just at threshold we find first a short phase of free acceleration, later a diffusion which is lower than predicted by the theoretical formula. The best way to analyze the problem is in terms of cumulants, but a reliable comparison with any theory requires also a time integration of the corresponding kinetic equations. (orig.)

  4. Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

    Directory of Open Access Journals (Sweden)

    Jae Sang Moon

    2017-12-01

    Full Text Available Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES. Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.

  5. Analysis of a Stochastic Chemical System Close to a SNIPER Bifurcation of Its Mean-Field Model

    KAUST Repository

    Erban, Radek

    2009-01-01

    A framework for the analysis of stochastic models of chemical systems for which the deterministic mean-field description is undergoing a saddle-node infinite period (SNIPER) bifurcation is presented. Such a bifurcation occurs, for example, in the modeling of cell-cycle regulation. It is shown that the stochastic system possesses oscillatory solutions even for parameter values for which the mean-field model does not oscillate. The dependence of the mean period of these oscillations on the parameters of the model (kinetic rate constants) and the size of the system (number of molecules present) are studied. Our approach is based on the chemical Fokker-Planck equation. To gain some insight into the advantages and disadvantages of the method, a simple one-dimensional chemical switch is first analyzed, and then the chemical SNIPER problem is studied in detail. First, results obtained by solving the Fokker-Planck equation numerically are presented. Then an asymptotic analysis of the Fokker-Planck equation is used to derive explicit formulae for the period of oscillation as a function of the rate constants and as a function of the system size. © 2009 Society for Industrial and Applied Mathematics.

  6. Stochastic Watershed Models for Risk Based Decision Making

    Science.gov (United States)

    Vogel, R. M.

    2017-12-01

    Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

  7. A Stochastic Maximum Principle for a Stochastic Differential Game of a Mean-Field Type

    Energy Technology Data Exchange (ETDEWEB)

    Hosking, John Joseph Absalom, E-mail: j.j.a.hosking@cma.uio.no [University of Oslo, Centre of Mathematics for Applications (CMA) (Norway)

    2012-12-15

    We construct a stochastic maximum principle (SMP) which provides necessary conditions for the existence of Nash equilibria in a certain form of N-agent stochastic differential game (SDG) of a mean-field type. The information structure considered for the SDG is of a possible asymmetric and partial type. To prove our SMP we take an approach based on spike-variations and adjoint representation techniques, analogous to that of S. Peng (SIAM J. Control Optim. 28(4):966-979, 1990) in the optimal stochastic control context. In our proof we apply adjoint representation procedures at three points. The first-order adjoint processes are defined as solutions to certain mean-field backward stochastic differential equations, and second-order adjoint processes of a first type are defined as solutions to certain backward stochastic differential equations. Second-order adjoint processes of a second type are defined as solutions of certain backward stochastic equations of a type that we introduce in this paper, and which we term conditional mean-field backward stochastic differential equations. From the resulting representations, we show that the terms relating to these second-order adjoint processes of the second type are of an order such that they do not appear in our final SMP equations. A comparable situation exists in an article by R. Buckdahn, B. Djehiche, and J. Li (Appl. Math. Optim. 64(2):197-216, 2011) that constructs a SMP for a mean-field type optimal stochastic control problem; however, the approach we take of using these second-order adjoint processes of a second type to deal with the type of terms that we refer to as the second form of quadratic-type terms represents an alternative to a development, to our setting, of the approach used in their article for their analogous type of term.

  8. A Stochastic Maximum Principle for a Stochastic Differential Game of a Mean-Field Type

    International Nuclear Information System (INIS)

    Hosking, John Joseph Absalom

    2012-01-01

    We construct a stochastic maximum principle (SMP) which provides necessary conditions for the existence of Nash equilibria in a certain form of N-agent stochastic differential game (SDG) of a mean-field type. The information structure considered for the SDG is of a possible asymmetric and partial type. To prove our SMP we take an approach based on spike-variations and adjoint representation techniques, analogous to that of S. Peng (SIAM J. Control Optim. 28(4):966–979, 1990) in the optimal stochastic control context. In our proof we apply adjoint representation procedures at three points. The first-order adjoint processes are defined as solutions to certain mean-field backward stochastic differential equations, and second-order adjoint processes of a first type are defined as solutions to certain backward stochastic differential equations. Second-order adjoint processes of a second type are defined as solutions of certain backward stochastic equations of a type that we introduce in this paper, and which we term conditional mean-field backward stochastic differential equations. From the resulting representations, we show that the terms relating to these second-order adjoint processes of the second type are of an order such that they do not appear in our final SMP equations. A comparable situation exists in an article by R. Buckdahn, B. Djehiche, and J. Li (Appl. Math. Optim. 64(2):197–216, 2011) that constructs a SMP for a mean-field type optimal stochastic control problem; however, the approach we take of using these second-order adjoint processes of a second type to deal with the type of terms that we refer to as the second form of quadratic-type terms represents an alternative to a development, to our setting, of the approach used in their article for their analogous type of term.

  9. Numerical computation of the transport matrix in toroidal plasma with a stochastic magnetic field

    Science.gov (United States)

    Zhu, Siqiang; Chen, Dunqiang; Dai, Zongliang; Wang, Shaojie

    2018-04-01

    A new numerical method, based on integrating along the full orbit of guiding centers, to compute the transport matrix is realized. The method is successfully applied to compute the phase-space diffusion tensor of passing electrons in a tokamak with a stochastic magnetic field. The new method also computes the Lagrangian correlation function, which can be used to evaluate the Lagrangian correlation time and the turbulence correlation length. For the case of the stochastic magnetic field, we find that the order of magnitude of the parallel correlation length can be estimated by qR0, as expected previously.

  10. Alfven-wave current drive and magnetic field stochasticity

    International Nuclear Information System (INIS)

    Litwin, C.; Hegna, C.C.

    1993-01-01

    Propagating Alfven waves can generate parallel current through an alpha effect. In resistive MHD however, the dynamo field is proportional to resistivity and as such cannot drive significant currents for realistic parameters. In the search for an enhancement of this effect the authors investigate the role of magnetic field stochasticity. They show that the presence of a stochastic magnetic field, either spontaneously generated by instabilities or induced externally, can enhance the alpha effect of the wave. This enhancement is caused by an increased wave dissipation due to both current diffusion and filamentation. For the range of parameters of current drive experiments at Phaedrus-T tokamak, a moderate field stochasticity leads to significant modifications in the loop voltage

  11. Nelson's stochastic quantization of free linearized gravitational field and its Markovian structure

    International Nuclear Information System (INIS)

    Lim, S.C.

    1983-05-01

    It is shown that by applying Nelson's stochastic quantization scheme to free linearized gravitational field tensor one can associate with the resulting stochastic system a stochastic tensor field which coincides with the ''space'' part of the Riemannian tensor in Euclidean space-time. However, such a stochastic field fails to satisfy the Markov property. Instead, it satisfies the reflection positivity. The Markovian structure of the stochastic fields associated with the electromagnetic field is also discussed. (author)

  12. Stochastic Wake Modelling Based on POD Analysis

    Directory of Open Access Journals (Sweden)

    David Bastine

    2018-03-01

    Full Text Available In this work, large eddy simulation data is analysed to investigate a new stochastic modeling approach for the wake of a wind turbine. The data is generated by the large eddy simulation (LES model PALM combined with an actuator disk with rotation representing the turbine. After applying a proper orthogonal decomposition (POD, three different stochastic models for the weighting coefficients of the POD modes are deduced resulting in three different wake models. Their performance is investigated mainly on the basis of aeroelastic simulations of a wind turbine in the wake. Three different load cases and their statistical characteristics are compared for the original LES, truncated PODs and the stochastic wake models including different numbers of POD modes. It is shown that approximately six POD modes are enough to capture the load dynamics on large temporal scales. Modeling the weighting coefficients as independent stochastic processes leads to similar load characteristics as in the case of the truncated POD. To complete this simplified wake description, we show evidence that the small-scale dynamics can be captured by adding to our model a homogeneous turbulent field. In this way, we present a procedure to derive stochastic wake models from costly computational fluid dynamics (CFD calculations or elaborated experimental investigations. These numerically efficient models provide the added value of possible long-term studies. Depending on the aspects of interest, different minimalized models may be obtained.

  13. Dynamic behaviors of spin-1/2 bilayer system within Glauber-type stochastic dynamics based on the effective-field theory

    International Nuclear Information System (INIS)

    Ertaş, Mehmet; Kantar, Ersin; Keskin, Mustafa

    2014-01-01

    The dynamic phase transitions (DPTs) and dynamic phase diagrams of the kinetic spin-1/2 bilayer system in the presence of a time-dependent oscillating external magnetic field are studied by using Glauber-type stochastic dynamics based on the effective-field theory with correlations for the ferromagnetic/ferromagnetic (FM/FM), antiferromagnetic/ferromagnetic (AFM/FM) and antiferromagnetic/antiferromagnetic (AFM/AFM) interactions. The time variations of average magnetizations and the temperature dependence of the dynamic magnetizations are investigated. The dynamic phase diagrams for the amplitude of the oscillating field versus temperature were presented. The results are compared with the results of the same system within Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • The Ising bilayer system is investigated within the Glauber dynamics based on EFT. • The time variations of average order parameters to find phases are studied. • The dynamic phase diagrams are found for the different interaction parameters. • The system displays the critical points as well as a re-entrant behavior

  14. Dynamic behaviors of spin-1/2 bilayer system within Glauber-type stochastic dynamics based on the effective-field theory

    Energy Technology Data Exchange (ETDEWEB)

    Ertaş, Mehmet; Kantar, Ersin, E-mail: ersinkantar@erciyes.edu.tr; Keskin, Mustafa

    2014-05-01

    The dynamic phase transitions (DPTs) and dynamic phase diagrams of the kinetic spin-1/2 bilayer system in the presence of a time-dependent oscillating external magnetic field are studied by using Glauber-type stochastic dynamics based on the effective-field theory with correlations for the ferromagnetic/ferromagnetic (FM/FM), antiferromagnetic/ferromagnetic (AFM/FM) and antiferromagnetic/antiferromagnetic (AFM/AFM) interactions. The time variations of average magnetizations and the temperature dependence of the dynamic magnetizations are investigated. The dynamic phase diagrams for the amplitude of the oscillating field versus temperature were presented. The results are compared with the results of the same system within Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • The Ising bilayer system is investigated within the Glauber dynamics based on EFT. • The time variations of average order parameters to find phases are studied. • The dynamic phase diagrams are found for the different interaction parameters. • The system displays the critical points as well as a re-entrant behavior.

  15. Stochastic neuron models

    CERN Document Server

    Greenwood, Priscilla E

    2016-01-01

    This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...

  16. A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

    Directory of Open Access Journals (Sweden)

    Jianxun Zhang

    2017-10-01

    Full Text Available A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

  17. From complex to simple: interdisciplinary stochastic models

    International Nuclear Information System (INIS)

    Mazilu, D A; Zamora, G; Mazilu, I

    2012-01-01

    We present two simple, one-dimensional, stochastic models that lead to a qualitative understanding of very complex systems from biology, nanoscience and social sciences. The first model explains the complicated dynamics of microtubules, stochastic cellular highways. Using the theory of random walks in one dimension, we find analytical expressions for certain physical quantities, such as the time dependence of the length of the microtubules, and diffusion coefficients. The second one is a stochastic adsorption model with applications in surface deposition, epidemics and voter systems. We introduce the ‘empty interval method’ and show sample calculations for the time-dependent particle density. These models can serve as an introduction to the field of non-equilibrium statistical physics, and can also be used as a pedagogical tool to exemplify standard statistical physics concepts, such as random walks or the kinetic approach of the master equation. (paper)

  18. Population stochastic modelling (PSM)--an R package for mixed-effects models based on stochastic differential equations.

    Science.gov (United States)

    Klim, Søren; Mortensen, Stig Bousgaard; Kristensen, Niels Rode; Overgaard, Rune Viig; Madsen, Henrik

    2009-06-01

    The extension from ordinary to stochastic differential equations (SDEs) in pharmacokinetic and pharmacodynamic (PK/PD) modelling is an emerging field and has been motivated in a number of articles [N.R. Kristensen, H. Madsen, S.H. Ingwersen, Using stochastic differential equations for PK/PD model development, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 109-141; C.W. Tornøe, R.V. Overgaard, H. Agersø, H.A. Nielsen, H. Madsen, E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations, Pharm. Res. 22 (August(8)) (2005) 1247-1258; R.V. Overgaard, N. Jonsson, C.W. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 85-107; U. Picchini, S. Ditlevsen, A. De Gaetano, Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics, Math. Med. Biol. 25 (June(2)) (2008) 141-155]. PK/PD models are traditionally based ordinary differential equations (ODEs) with an observation link that incorporates noise. This state-space formulation only allows for observation noise and not for system noise. Extending to SDEs allows for a Wiener noise component in the system equations. This additional noise component enables handling of autocorrelated residuals originating from natural variation or systematic model error. Autocorrelated residuals are often partly ignored in PK/PD modelling although violating the hypothesis for many standard statistical tests. This article presents a package for the statistical program R that is able to handle SDEs in a mixed-effects setting. The estimation method implemented is the FOCE(1) approximation to the population likelihood which is generated from the individual likelihoods that are approximated using the Extended Kalman Filter's one-step predictions.

  19. Identifiability in stochastic models

    CERN Document Server

    1992-01-01

    The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.

  20. Stochastic quantization and topological theories

    International Nuclear Information System (INIS)

    Fainberg, V.Y.; Subbotin, A.V.; Kuznetsov, A.N.

    1992-01-01

    In the last two years topological quantum field theories (TQFT) have attached much attention. This paper reports that from the very beginning it was realized that due to a peculiar BRST-like symmetry these models admitted so-called Nicolai mapping: the Nicolai variables, in terms of which actions of the theories become gaussian, are nothing but (anti-) selfduality conditions or their generalizations. This fact became a starting point in the quest of possible stochastic interpretation to topological field theories. The reasons behind were quite simple and included, in particular, the well-known relations between stochastic processes and supersymmetry. The main goal would have been achieved, if it were possible to construct stochastic processes governed by Langevin or Fokker-Planck equations in a real Euclidean time leading to TQFT's path integrals (equivalently: to reformulate TQFTs as non-equilibrium phase dynamics of stochastic processes). Further on, if it would appear that these processes correspond to the stochastic quantization of theories of some definite kind, one could expect (d + 1)-dimensional TQFTs to share some common properties with d-dimensional ones

  1. A Stochastic Maximum Principle for Risk-Sensitive Mean-Field Type Control

    KAUST Repository

    Djehiche, Boualem; Tembine, Hamidou; Tempone, Raul

    2015-01-01

    In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle (SMP) for optimal control of stochastic differential equations (SDEs) of mean-field type, in which the drift and the diffusion coefficients as well as the performance functional depend not only on the state and the control but also on the mean of the distribution of the state. Our result extends the risk-sensitive SMP (without mean-field coupling) of Lim and Zhou (2005), derived for feedback (or Markov) type optimal controls, to optimal control problems for non-Markovian dynamics which may be time-inconsistent in the sense that the Bellman optimality principle does not hold. In our approach to the risk-sensitive SMP, the smoothness assumption on the value-function imposed in Lim and Zhou (2005) needs not be satisfied. For a general action space a Peng's type SMP is derived, specifying the necessary conditions for optimality. Two examples are carried out to illustrate the proposed risk-sensitive mean-field type SMP under linear stochastic dynamics with exponential quadratic cost function. Explicit solutions are given for both mean-field free and mean-field models.

  2. A Stochastic Maximum Principle for Risk-Sensitive Mean-Field Type Control

    KAUST Repository

    Djehiche, Boualem

    2015-02-24

    In this paper we study mean-field type control problems with risk-sensitive performance functionals. We establish a stochastic maximum principle (SMP) for optimal control of stochastic differential equations (SDEs) of mean-field type, in which the drift and the diffusion coefficients as well as the performance functional depend not only on the state and the control but also on the mean of the distribution of the state. Our result extends the risk-sensitive SMP (without mean-field coupling) of Lim and Zhou (2005), derived for feedback (or Markov) type optimal controls, to optimal control problems for non-Markovian dynamics which may be time-inconsistent in the sense that the Bellman optimality principle does not hold. In our approach to the risk-sensitive SMP, the smoothness assumption on the value-function imposed in Lim and Zhou (2005) needs not be satisfied. For a general action space a Peng\\'s type SMP is derived, specifying the necessary conditions for optimality. Two examples are carried out to illustrate the proposed risk-sensitive mean-field type SMP under linear stochastic dynamics with exponential quadratic cost function. Explicit solutions are given for both mean-field free and mean-field models.

  3. Thermodynamically Consistent Algorithms for the Solution of Phase-Field Models

    KAUST Repository

    Vignal, Philippe

    2016-01-01

    of thermodynamically consistent algorithms for time integration of phase-field models. The first part of this thesis focuses on an energy-stable numerical strategy developed for the phase-field crystal equation. This model was put forward to model microstructure

  4. Persistence and extinction for a stochastic logistic model with infinite delay

    Directory of Open Access Journals (Sweden)

    Chun Lu

    2013-11-01

    Full Text Available This article, studies a stochastic logistic model with infinite delay. Using a phase space, we establish sufficient conditions for the extinction, nonpersistence in the mean, weak persistence, and stochastic permanence. A threshold between weak persistence and extinction is obtained. Our results state that different types of environmental noises have different effects on the persistence and extinction, and that the delay has no impact on the persistence and extinction for the stochastic model in the autonomous case. Numerical simulations illustrate the theoretical results.

  5. Benchmark problems for numerical implementations of phase field models

    International Nuclear Information System (INIS)

    Jokisaari, A. M.; Voorhees, P. W.; Guyer, J. E.; Warren, J.; Heinonen, O. G.

    2016-01-01

    Here, we present the first set of benchmark problems for phase field models that are being developed by the Center for Hierarchical Materials Design (CHiMaD) and the National Institute of Standards and Technology (NIST). While many scientific research areas use a limited set of well-established software, the growing phase field community continues to develop a wide variety of codes and lacks benchmark problems to consistently evaluate the numerical performance of new implementations. Phase field modeling has become significantly more popular as computational power has increased and is now becoming mainstream, driving the need for benchmark problems to validate and verify new implementations. We follow the example set by the micromagnetics community to develop an evolving set of benchmark problems that test the usability, computational resources, numerical capabilities and physical scope of phase field simulation codes. In this paper, we propose two benchmark problems that cover the physics of solute diffusion and growth and coarsening of a second phase via a simple spinodal decomposition model and a more complex Ostwald ripening model. We demonstrate the utility of benchmark problems by comparing the results of simulations performed with two different adaptive time stepping techniques, and we discuss the needs of future benchmark problems. The development of benchmark problems will enable the results of quantitative phase field models to be confidently incorporated into integrated computational materials science and engineering (ICME), an important goal of the Materials Genome Initiative.

  6. Modelling of diesel spray flame under engine-like conditions using an accelerated eulerian stochastic fields method: A convergence study of the number of stochastic fields

    DEFF Research Database (Denmark)

    Pang, Kar Mun; Jangi, Mehdi; Bai, X.-S.

    generated similar results. The principal motivation for ESF compared to Lagrangian particle based PDF is the relative ease of implementation of the former into Eulerian computational fluid dynamics(CFD) codes [5]. Several works have attempted to implement the ESF model for the simulations of diesel spray......The use of transported Probability Density Function(PDF) methods allows a single model to compute the autoignition, premixed mode and diffusion flame of diesel combustion under engine-like conditions [1,2]. The Lagrangian particle based transported PDF models have been validated across a wide range...... combustion under engine-like conditions.The current work aims to further evaluate the performance of the ESF model in this application, with an emphasis on examining the convergence of the number of stochastic fields, nsf. Five test conditions, covering both the conventional diesel combustion and low...

  7. Two new algorithms to combine kriging with stochastic modelling

    Science.gov (United States)

    Venema, Victor; Lindau, Ralf; Varnai, Tamas; Simmer, Clemens

    2010-05-01

    Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated driven by such a kriged field. Stochastic modelling aims at reproducing the statistical structure of the data in space and time. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. While stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. This requires the use of so-called constrained stochastic models. Because radiative transfer through clouds is a highly nonlinear process, it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately. In addition, the correlations within the cloud field are important, especially because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. Up to now, however, we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. This algorithm is similar to the standard iterative amplitude adjusted Fourier transform (IAAFT) algorithm, but has an additional iterative step in which the surrogate field is nudged towards the kriged field. The nudging strength is gradually

  8. Stochastic inflation and nonlinear gravity

    International Nuclear Information System (INIS)

    Salopek, D.S.; Bond, J.R.

    1991-01-01

    We show how nonlinear effects of the metric and scalar fields may be included in stochastic inflation. Our formalism can be applied to non-Gaussian fluctuation models for galaxy formation. Fluctuations with wavelengths larger than the horizon length are governed by a network of Langevin equations for the physical fields. Stochastic noise terms arise from quantum fluctuations that are assumed to become classical at horizon crossing and that then contribute to the background. Using Hamilton-Jacobi methods, we solve the Arnowitt-Deser-Misner constraint equations which allows us to separate the growing modes from the decaying ones in the drift phase following each stochastic impulse. We argue that the most reasonable choice of time hypersurfaces for the Langevin system during inflation is T=ln(Ha), where H and a are the local values of the Hubble parameter and the scale factor, since T is the natural time for evolving the short-wavelength scalar field fluctuations in an inhomogeneous background

  9. Stabilization and destabilization effects of the electric field on stochastic precipitate pattern

    NARCIS (Netherlands)

    Lagzi, István; Izsak, F.

    2004-01-01

    Stabilization and destabilization effects of an applied electric field on the Liesegang pattern formation in low concentration gradient were studied with numerical model simulations. In the absence of an electric field pattern formation exhibits increasingly stochastic behaviour as the initial

  10. Thermodynamically Consistent Algorithms for the Solution of Phase-Field Models

    KAUST Repository

    Vignal, Philippe

    2016-02-11

    Phase-field models are emerging as a promising strategy to simulate interfacial phenomena. Rather than tracking interfaces explicitly as done in sharp interface descriptions, these models use a diffuse order parameter to monitor interfaces implicitly. This implicit description, as well as solid physical and mathematical footings, allow phase-field models to overcome problems found by predecessors. Nonetheless, the method has significant drawbacks. The phase-field framework relies on the solution of high-order, nonlinear partial differential equations. Solving these equations entails a considerable computational cost, so finding efficient strategies to handle them is important. Also, standard discretization strategies can many times lead to incorrect solutions. This happens because, for numerical solutions to phase-field equations to be valid, physical conditions such as mass conservation and free energy monotonicity need to be guaranteed. In this work, we focus on the development of thermodynamically consistent algorithms for time integration of phase-field models. The first part of this thesis focuses on an energy-stable numerical strategy developed for the phase-field crystal equation. This model was put forward to model microstructure evolution. The algorithm developed conserves, guarantees energy stability and is second order accurate in time. The second part of the thesis presents two numerical schemes that generalize literature regarding energy-stable methods for conserved and non-conserved phase-field models. The time discretization strategies can conserve mass if needed, are energy-stable, and second order accurate in time. We also develop an adaptive time-stepping strategy, which can be applied to any second-order accurate scheme. This time-adaptive strategy relies on a backward approximation to give an accurate error estimator. The spatial discretization, in both parts, relies on a mixed finite element formulation and isogeometric analysis. The codes are

  11. Nonlocal quantum field theory and stochastic quantum mechanics

    International Nuclear Information System (INIS)

    Namsrai, K.

    1986-01-01

    This volume presents a systematic development of the implications to both quantum mechanics and quantum field theory of the hypothesis of a stochastic structure of space-time. Some applications to elementary particle physics are also considered. Part 1 is concerned with nonlocal quantum field theory and, among other topics, deals with quantized fields, electromagnetic and weak processes, the Schroedinger equation, and functional methods and their applications. Part 2 presents an introduction to stochastic mechanics and many specific problems of interest are discussed. (Auth.)

  12. Approximate models for broken clouds in stochastic radiative transfer theory

    International Nuclear Information System (INIS)

    Doicu, Adrian; Efremenko, Dmitry S.; Loyola, Diego; Trautmann, Thomas

    2014-01-01

    This paper presents approximate models in stochastic radiative transfer theory. The independent column approximation and its modified version with a solar source computed in a full three-dimensional atmosphere are formulated in a stochastic framework and for arbitrary cloud statistics. The nth-order stochastic models describing the independent column approximations are equivalent to the nth-order stochastic models for the original radiance fields in which the gradient vectors are neglected. Fast approximate models are further derived on the basis of zeroth-order stochastic models and the independent column approximation. The so-called “internal mixing” models assume a combination of the optical properties of the cloud and the clear sky, while the “external mixing” models assume a combination of the radiances corresponding to completely overcast and clear skies. A consistent treatment of internal and external mixing models is provided, and a new parameterization of the closure coefficient in the effective thickness approximation is given. An efficient computation of the closure coefficient for internal mixing models, using a previously derived vector stochastic model as a reference, is also presented. Equipped with appropriate look-up tables for the closure coefficient, these models can easily be integrated into operational trace gas retrieval systems that exploit absorption features in the near-IR solar spectrum. - Highlights: • Independent column approximation in a stochastic setting. • Fast internal and external mixing models for total and diffuse radiances. • Efficient optimization of internal mixing models to match reference models

  13. A stochastic differential equation framework for the turbulent velocity field

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Schmiegel, Jürgen

    We discuss a stochastic differential equation, as a modelling framework for the turbulent velocity field, that is capable of capturing basic stylized facts of the statistics of velocity increments. In particular, we focus on the evolution of the probability density of velocity increments...

  14. Phase field model for the study of boiling

    International Nuclear Information System (INIS)

    Ruyer, P.

    2006-07-01

    This study concerns both the modeling and the numerical simulation of boiling flows. First we propose a review concerning nucleate boiling at high wall heat flux and focus more particularly on the current understanding of the boiling crisis. From this analysis we deduce a motivation for the numerical simulation of bubble growth dynamics. The main and remaining part of this study is then devoted to the development and analyze of a phase field model for the liquid-vapor flows with phase change. We propose a thermodynamic quasi-compressible formulation whose properties match the one required for the numerical study envisaged. The system of governing equations is a thermodynamically consistent regularization of the sharp interface model, that is the advantage of the di use interface models. We show that the thickness of the interface transition layer can be defined independently from the thermodynamic description of the bulk phases, a property that is numerically attractive. We derive the kinetic relation that allows to analyze the consequences of the phase field formulation on the model of the dissipative mechanisms. Finally we study the numerical resolution of the model with the help of simulations of phase transition in simple configurations as well as of isothermal bubble dynamics. (author)

  15. A stochastic aerodynamic model for stationary blades in unsteady 3D wind fields

    International Nuclear Information System (INIS)

    Fluck, Manuel; Crawford, Curran

    2016-01-01

    Dynamic loads play an important roll in the design of wind turbines, but establishing the life-time aerodynamic loads (e.g. extreme and fatigue loads) is a computationally expensive task. Conventional (deterministic) methods to analyze long term loads, which rely on the repeated analysis of multiple different wind samples, are usually too expensive to be included in optimization routines. We present a new stochastic approach, which solves the aerodynamic system equations (Lagrangian vortex model) in the stochastic space, and thus arrive directly at a stochastic description of the coupled loads along a turbine blade. This new approach removes the requirement of analyzing multiple different realizations. Instead, long term loads can be extracted from a single stochastic solution, a procedure that is obviously significantly faster. Despite the reduced analysis time, results obtained from the stochastic approach match deterministic result well for a simple test-case (a stationary blade). In future work, the stochastic method will be extended to rotating blades, thus opening up new avenues to include long term loads into turbine optimization. (paper)

  16. BRS invariant stochastic quantization of Einstein gravity

    International Nuclear Information System (INIS)

    Nakazawa, Naohito.

    1989-11-01

    We study stochastic quantization of gravity in terms of a BRS invariant canonical operator formalism. By introducing artificially canonical momentum variables for the original field variables, a canonical formulation of stochastic quantization is proposed in the sense that the Fokker-Planck hamiltonian is the generator of the fictitious time translation. Then we show that there exists a nilpotent BRS symmetry in an enlarged phase space of the first-class constrained systems. The phase space is spanned by the dynamical variables, their canonical conjugate momentum variables, Faddeev-Popov ghost and anti-ghost. We apply the general BRS invariant formulation to stochastic quantization of gravity which is described as a second-class constrained system in terms of a pair of Langevin equations coupled with white noises. It is shown that the stochastic action of gravity includes explicitly the De Witt's type superspace metric which leads to a geometrical interpretation of quantum gravity analogous to nonlinear σ-models. (author)

  17. Stochastic Modeling and Deterministic Limit of Catalytic Surface Processes

    DEFF Research Database (Denmark)

    Starke, Jens; Reichert, Christian; Eiswirth, Markus

    2007-01-01

    of stochastic origin can be observed in experiments. The models include a new approach to the platinum phase transition, which allows for a unification of existing models for Pt(100) and Pt(110). The rich nonlinear dynamical behavior of the macroscopic reaction kinetics is investigated and shows good agreement...

  18. A probabilistic graphical model based stochastic input model construction

    International Nuclear Information System (INIS)

    Wan, Jiang; Zabaras, Nicholas

    2014-01-01

    Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media

  19. Electron Acceleration by Stochastic Electric Fields in Thunderstorms: Terrestrial Gamma-Ray Flashes

    Science.gov (United States)

    Alnussirat, S.; Miller, J. A.; Christian, H. J., Jr.; Fishman, G. J.

    2016-12-01

    Terrestrial gamma-ray flashes (TGFs) are energetic pulses of photons, which are intense and short, originating in the atmosphere during thunderstorm activity. Despite the number of observations, the production mechanism(s) of TGFs and other energetic particles is not well understood. However, two mechanisms have been suggested as a source of TGFs: (1) the relativistic runaway electron avalanche mechanism (RREA), and (2) the lightning leader mechanism. The RREA can account for the TGF observations, but requires restrictive or unrealistic assumptions. The lightning leader channel is also expected to produce runaway electrons, but through inhomogeneous, small scale, strong electric fields. In this work we use the Boltzmann equation to model the electron acceleration by the lightning leader mechanism, and we derive the gamma-ray spectrum from the electron distribution function. The electric fields at the tip of the leaders are assumed to be stochastic in space and time. Since the physics involved in the lightening leader is not known, we test different cases of the stochastic acceleration agent. From this modeling we hope to investigate the possibility and efficiency of stochastic acceleration in thunderstorm.

  20. Phase-field modeling of corrosion kinetics under dual-oxidants

    Science.gov (United States)

    Wen, You-Hai; Chen, Long-Qing; Hawk, Jeffrey A.

    2012-04-01

    A phase-field model is proposed to simulate corrosion kinetics under a dual-oxidant atmosphere. It will be demonstrated that the model can be applied to simulate corrosion kinetics under oxidation, sulfidation and simultaneous oxidation/sulfidation processes. Phase-dependent diffusivities are incorporated in a natural manner and allow more realistic modeling as the diffusivities usually differ by many orders of magnitude in different phases. Simple free energy models are then used for testing the model while calibrated free energy models can be implemented for quantitative modeling.

  1. ARMA modeling of stochastic processes in nuclear reactor with significant detection noise

    International Nuclear Information System (INIS)

    Zavaljevski, N.

    1992-01-01

    The theoretical basis of ARMA modelling of stochastic processes in nuclear reactor was presented in a previous paper, neglecting observational noise. The identification of real reactor data indicated that in some experiments the detection noise is significant. Thus a more rigorous theoretical modelling of stochastic processes in nuclear reactor is performed. Starting from the fundamental stochastic differential equations of the Langevin type for the interaction of the detector with neutron field, a new theoretical ARMA model is developed. preliminary identification results confirm the theoretical expectations. (author)

  2. Phase transitions in the random field Ising model in the presence of a transverse field

    Energy Technology Data Exchange (ETDEWEB)

    Dutta, A.; Chakrabarti, B.K. [Saha Institute of Nuclear Physics, Bidhannagar, Calcutta (India); Stinchcombe, R.B. [Saha Institute of Nuclear Physics, Bidhannagar, Calcutta (India); Department of Physics, Oxford (United Kingdom)

    1996-09-07

    We have studied the phase transition behaviour of the random field Ising model in the presence of a transverse (or tunnelling) field. The mean field phase diagram has been studied in detail, and in particular the nature of the transition induced by the tunnelling (transverse) field at zero temperature. Modified hyper-scaling relation for the zero-temperature transition has been derived using the Suzuki-Trotter formalism and a modified 'Harris criterion'. Mapping of the model to a randomly diluted antiferromagnetic Ising model in uniform longitudinal and transverse field is also given. (author)

  3. A stochastic mathematical model to locate field hospitals under disruption uncertainty for large-scale disaster preparedness

    Directory of Open Access Journals (Sweden)

    Nezir Aydin

    2016-03-01

    Full Text Available In this study, we consider field hospital location decisions for emergency treatment points in response to large scale disasters. Specifically, we developed a two-stage stochastic model that determines the number and locations of field hospitals and the allocation of injured victims to these field hospitals. Our model considers the locations as well as the failings of the existing public hospitals while deciding on the location of field hospitals that are anticipated to be opened. The model that we developed is a variant of the P-median location model and it integrates capacity restrictions both on field hospitals that are planned to be opened and the disruptions that occur in existing public hospitals. We conducted experiments to demonstrate how the proposed model can be utilized in practice in a real life problem case scenario. Results show the effects of the failings of existing hospitals, the level of failure probability and the capacity of projected field hospitals to deal with the assessment of any given emergency treatment system’s performance. Crucially, it also specifically provides an assessment on the average distance within which a victim needs to be transferred in order to be treated properly and then from this assessment, the proportion of total satisfied demand is then calculated.

  4. Stochastic tools in turbulence

    CERN Document Server

    Lumey, John L

    2012-01-01

    Stochastic Tools in Turbulence discusses the available mathematical tools to describe stochastic vector fields to solve problems related to these fields. The book deals with the needs of turbulence in relation to stochastic vector fields, particularly, on three-dimensional aspects, linear problems, and stochastic model building. The text describes probability distributions and densities, including Lebesgue integration, conditional probabilities, conditional expectations, statistical independence, lack of correlation. The book also explains the significance of the moments, the properties of the

  5. A positivity preserving and conservative variational scheme for phase-field modeling of two-phase flows

    Science.gov (United States)

    Joshi, Vaibhav; Jaiman, Rajeev K.

    2018-05-01

    We present a positivity preserving variational scheme for the phase-field modeling of incompressible two-phase flows with high density ratio. The variational finite element technique relies on the Allen-Cahn phase-field equation for capturing the phase interface on a fixed Eulerian mesh with mass conservative and energy-stable discretization. The mass conservation is achieved by enforcing a Lagrange multiplier which has both temporal and spatial dependence on the underlying solution of the phase-field equation. To make the scheme energy-stable in a variational sense, we discretize the spatial part of the Lagrange multiplier in the phase-field equation by the mid-point approximation. The proposed variational technique is designed to reduce the spurious and unphysical oscillations in the solution while maintaining the second-order accuracy of both spatial and temporal discretizations. We integrate the Allen-Cahn phase-field equation with the incompressible Navier-Stokes equations for modeling a broad range of two-phase flow and fluid-fluid interface problems. The coupling of the implicit discretizations corresponding to the phase-field and the incompressible flow equations is achieved via nonlinear partitioned iterative procedure. Comparison of results between the standard linear stabilized finite element method and the present variational formulation shows a remarkable reduction of oscillations in the solution while retaining the boundedness of the phase-indicator field. We perform a standalone test to verify the accuracy and stability of the Allen-Cahn two-phase solver. We examine the convergence and accuracy properties of the coupled phase-field solver through the standard benchmarks of the Laplace-Young law and a sloshing tank problem. Two- and three-dimensional dam break problems are simulated to assess the capability of the phase-field solver for complex air-water interfaces involving topological changes on unstructured meshes. Finally, we demonstrate the phase-field

  6. Phase-field modelling of microstructural evolution and properties

    Science.gov (United States)

    Zhu, Jingzhi

    As one of the most powerful techniques in computational materials science, the diffuse-interface phase-field model has been widely employed for simulating various meso-scale microstructural evolution processes. The main purpose of this thesis is to develop a quantitative phase-field model for predicting microstructures and properties in real alloy systems which can be linked to existing thermodynamic/kinetic databases and parameters obtained from experimental measurements or first-principle calculations. To achieve this goal; many factors involved in complicated real systems are investigated, many of which are often simplified or ignored in existing models, e.g. the dependence of diffusional atomic mobility and elastic constants on composition. Efficient numerical techniques must be developed to solve those partial differential equations that are involved in modelling microstructural evolutions and properties. In this thesis, different spectral methods were proposed for the time-dependent phase-field kinetic equations and diffusion equations. For solving the elastic equilibrium equation with the consideration of elastic inhomogeneity, a conjugate gradient method was utilized. The numerical approaches developed were generally found to be more accurate and efficient than conventional approach such as finite difference method. A composition-dependent Cahn-Hilliard equation was solved by using a semi-implicit Fourier-spectral method. It was shown that the morphological evolutions in bulk-diffusion-controlled coarsening and interface-diffusion-controlled developed similar patterns and scaling behaviors. For bulk-diffusion-controlled coarsening, a cubic growth law was obeyed in the scaling regime, whereas a fourth power growth law was observed for interface-diffusion-controlled coarsening. The characteristics of a microstructure under the influence of elastic energy depend on elastic properties such as elastic anisotropy, lattice mismatch, elastic inhomogeneity and

  7. Modeling animal movements using stochastic differential equations

    Science.gov (United States)

    Haiganoush K. Preisler; Alan A. Ager; Bruce K. Johnson; John G. Kie

    2004-01-01

    We describe the use of bivariate stochastic differential equations (SDE) for modeling movements of 216 radiocollared female Rocky Mountain elk at the Starkey Experimental Forest and Range in northeastern Oregon. Spatially and temporally explicit vector fields were estimated using approximating difference equations and nonparametric regression techniques. Estimated...

  8. Phase-Field Modeling of Sigma-Phase Precipitation in 25Cr7Ni4Mo Duplex Stainless Steel

    Science.gov (United States)

    Malik, Amer; Odqvist, Joakim; Höglund, Lars; Hertzman, Staffan; Ågren, John

    2017-10-01

    Phase-field modeling is used to simulate the formation of sigma phase in a model alloy mimicking a commercial super duplex stainless steel (SDSS) alloy, in order to study precipitation and growth of sigma phase under linear continuous cooling. The so-called Warren-Boettinger-McFadden (WBM) model is used to build the basis of the multiphase and multicomponent phase-field model. The thermodynamic inconsistency at the multiple junctions associated with the multiphase formulation of the WBM model is resolved by means of a numerical Cut-off algorithm. To make realistic simulations, all the kinetic and the thermodynamic quantities are derived from the CALPHAD databases at each numerical time step, using Thermo-Calc and TQ-Interface. The credibility of the phase-field model is verified by comparing the results from the phase-field simulations with the corresponding DICTRA simulations and also with the empirical data. 2D phase-field simulations are performed for three different cooling rates in two different initial microstructures. A simple model for the nucleation of sigma phase is also implemented in the first case. Simulation results show that the precipitation of sigma phase is characterized by the accumulation of Cr and Mo at the austenite-ferrite and the ferrite-ferrite boundaries. Moreover, it is observed that a slow cooling rate promotes the growth of sigma phase, while a higher cooling rate restricts it, eventually preserving the duplex structure in the SDSS alloy. Results from the phase-field simulations are also compared quantitatively with the experiments, performed on a commercial 2507 SDSS alloy. It is found that overall, the predicted morphological features of the transformation and the composition profiles show good conformity with the empirical data.

  9. A stochastic asymptotic-preserving scheme for a kinetic-fluid model for disperse two-phase flows with uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Shi, E-mail: sjin@wisc.edu [Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706 (United States); Institute of Natural Sciences, School of Mathematical Science, MOELSEC and SHL-MAC, Shanghai Jiao Tong University, Shanghai 200240 (China); Shu, Ruiwen, E-mail: rshu2@math.wisc.edu [Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706 (United States)

    2017-04-15

    In this paper we consider a kinetic-fluid model for disperse two-phase flows with uncertainty. We propose a stochastic asymptotic-preserving (s-AP) scheme in the generalized polynomial chaos stochastic Galerkin (gPC-sG) framework, which allows the efficient computation of the problem in both kinetic and hydrodynamic regimes. The s-AP property is proved by deriving the equilibrium of the gPC version of the Fokker–Planck operator. The coefficient matrices that arise in a Helmholtz equation and a Poisson equation, essential ingredients of the algorithms, are proved to be positive definite under reasonable and mild assumptions. The computation of the gPC version of a translation operator that arises in the inversion of the Fokker–Planck operator is accelerated by a spectrally accurate splitting method. Numerical examples illustrate the s-AP property and the efficiency of the gPC-sG method in various asymptotic regimes.

  10. The Long Time Behavior of a Stochastic Logistic Model with Infinite Delay and Impulsive Perturbation

    OpenAIRE

    Lu, Chun; Wu, Kaining

    2016-01-01

    This paper considers a stochastic logistic model with infinite delay and impulsive perturbation. Firstly, with the space $C_{g}$ as phase space, the definition of solution to a stochastic functional differential equation with infinite delay and impulsive perturbation is established. According to this definition, we show that our model has an unique global positive solution. Then we establish the sufficient and necessary conditions for extinction and stochastic permanence of the...

  11. Grassmann phase space methods for fermions. II. Field theory

    Energy Technology Data Exchange (ETDEWEB)

    Dalton, B.J., E-mail: bdalton@swin.edu.au [Centre for Quantum and Optical Science, Swinburne University of Technology, Melbourne, Victoria 3122 (Australia); Jeffers, J. [Department of Physics, University of Strathclyde, Glasgow G4ONG (United Kingdom); Barnett, S.M. [School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom)

    2017-02-15

    In both quantum optics and cold atom physics, the behaviour of bosonic photons and atoms is often treated using phase space methods, where mode annihilation and creation operators are represented by c-number phase space variables, with the density operator equivalent to a distribution function of these variables. The anti-commutation rules for fermion annihilation, creation operators suggests the possibility of using anti-commuting Grassmann variables to represent these operators. However, in spite of the seminal work by Cahill and Glauber and a few applications, the use of Grassmann phase space methods in quantum-atom optics to treat fermionic systems is rather rare, though fermion coherent states using Grassmann variables are widely used in particle physics. This paper presents a phase space theory for fermion systems based on distribution functionals, which replace the density operator and involve Grassmann fields representing anti-commuting fermion field annihilation, creation operators. It is an extension of a previous phase space theory paper for fermions (Paper I) based on separate modes, in which the density operator is replaced by a distribution function depending on Grassmann phase space variables which represent the mode annihilation and creation operators. This further development of the theory is important for the situation when large numbers of fermions are involved, resulting in too many modes to treat separately. Here Grassmann fields, distribution functionals, functional Fokker–Planck equations and Ito stochastic field equations are involved. Typical applications to a trapped Fermi gas of interacting spin 1/2 fermionic atoms and to multi-component Fermi gases with non-zero range interactions are presented, showing that the Ito stochastic field equations are local in these cases. For the spin 1/2 case we also show how simple solutions can be obtained both for the untrapped case and for an optical lattice trapping potential.

  12. Grassmann phase space methods for fermions. II. Field theory

    International Nuclear Information System (INIS)

    Dalton, B.J.; Jeffers, J.; Barnett, S.M.

    2017-01-01

    In both quantum optics and cold atom physics, the behaviour of bosonic photons and atoms is often treated using phase space methods, where mode annihilation and creation operators are represented by c-number phase space variables, with the density operator equivalent to a distribution function of these variables. The anti-commutation rules for fermion annihilation, creation operators suggests the possibility of using anti-commuting Grassmann variables to represent these operators. However, in spite of the seminal work by Cahill and Glauber and a few applications, the use of Grassmann phase space methods in quantum-atom optics to treat fermionic systems is rather rare, though fermion coherent states using Grassmann variables are widely used in particle physics. This paper presents a phase space theory for fermion systems based on distribution functionals, which replace the density operator and involve Grassmann fields representing anti-commuting fermion field annihilation, creation operators. It is an extension of a previous phase space theory paper for fermions (Paper I) based on separate modes, in which the density operator is replaced by a distribution function depending on Grassmann phase space variables which represent the mode annihilation and creation operators. This further development of the theory is important for the situation when large numbers of fermions are involved, resulting in too many modes to treat separately. Here Grassmann fields, distribution functionals, functional Fokker–Planck equations and Ito stochastic field equations are involved. Typical applications to a trapped Fermi gas of interacting spin 1/2 fermionic atoms and to multi-component Fermi gases with non-zero range interactions are presented, showing that the Ito stochastic field equations are local in these cases. For the spin 1/2 case we also show how simple solutions can be obtained both for the untrapped case and for an optical lattice trapping potential.

  13. Recursive stochastic effects in valley hybrid inflation

    Science.gov (United States)

    Levasseur, Laurence Perreault; Vennin, Vincent; Brandenberger, Robert

    2013-10-01

    Hybrid inflation is a two-field model where inflation ends because of a tachyonic instability, the duration of which is determined by stochastic effects and has important observational implications. Making use of the recursive approach to the stochastic formalism presented in [L. P. Levasseur, preceding article, Phys. Rev. D 88, 083537 (2013)], these effects are consistently computed. Through an analysis of backreaction, this method is shown to converge in the valley but points toward an (expected) instability in the waterfall. It is further shown that the quasistationarity of the auxiliary field distribution breaks down in the case of a short-lived waterfall. We find that the typical dispersion of the waterfall field at the critical point is then diminished, thus increasing the duration of the waterfall phase and jeopardizing the possibility of a short transition. Finally, we find that stochastic effects worsen the blue tilt of the curvature perturbations by an O(1) factor when compared with the usual slow-roll contribution.

  14. Research on nonlinear stochastic dynamical price model

    International Nuclear Information System (INIS)

    Li Jiaorui; Xu Wei; Xie Wenxian; Ren Zhengzheng

    2008-01-01

    In consideration of many uncertain factors existing in economic system, nonlinear stochastic dynamical price model which is subjected to Gaussian white noise excitation is proposed based on deterministic model. One-dimensional averaged Ito stochastic differential equation for the model is derived by using the stochastic averaging method, and applied to investigate the stability of the trivial solution and the first-passage failure of the stochastic price model. The stochastic price model and the methods presented in this paper are verified by numerical studies

  15. Stochastic bifurcation in a model of love with colored noise

    Science.gov (United States)

    Yue, Xiaokui; Dai, Honghua; Yuan, Jianping

    2015-07-01

    In this paper, we wish to examine the stochastic bifurcation induced by multiplicative Gaussian colored noise in a dynamical model of love where the random factor is used to describe the complexity and unpredictability of psychological systems. First, the dynamics in deterministic love-triangle model are considered briefly including equilibrium points and their stability, chaotic behaviors and chaotic attractors. Then, the influences of Gaussian colored noise with different parameters are explored such as the phase plots, top Lyapunov exponents, stationary probability density function (PDF) and stochastic bifurcation. The stochastic P-bifurcation through a qualitative change of the stationary PDF will be observed and bifurcation diagram on parameter plane of correlation time and noise intensity is presented to find the bifurcation behaviors in detail. Finally, the top Lyapunov exponent is computed to determine the D-bifurcation when the noise intensity achieves to a critical value. By comparison, we find there is no connection between two kinds of stochastic bifurcation.

  16. Phase diagram of the mean field model of simplicial gravity

    International Nuclear Information System (INIS)

    Bialas, P.; Burda, Z.; Johnston, D.

    1999-01-01

    We discuss the phase diagram of the balls in boxes model, with a varying number of boxes. The model can be regarded as a mean-field model of simplicial gravity. We analyse in detail the case of weights of the form p(q) = q -β , which correspond to the measure term introduced in the simplicial quantum gravity simulations. The system has two phases: elongated (fluid) and crumpled. For β ε (2, ∞) the transition between these two phases is first-order, while for β ε (1, 2) it is continuous. The transition becomes softer when β approaches unity and eventually disappears at β = 1. We then generalise the discussion to an arbitrary set of weights. Finally, we show that if one introduces an additional kinematic bound on the average density of balls per box then a new condensed phase appears in the phase diagram. It bears some similarity to the crinkled phase of simplicial gravity discussed recently in models of gravity interacting with matter fields

  17. Alternative Asymmetric Stochastic Volatility Models

    NARCIS (Netherlands)

    M. Asai (Manabu); M.J. McAleer (Michael)

    2010-01-01

    textabstractThe stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is

  18. Differential form representation of stochastic electromagnetic fields

    Science.gov (United States)

    Haider, Michael; Russer, Johannes A.

    2017-09-01

    In this work, we revisit the theory of stochastic electromagnetic fields using exterior differential forms. We present a short overview as well as a brief introduction to the application of differential forms in electromagnetic theory. Within the framework of exterior calculus we derive equations for the second order moments, describing stochastic electromagnetic fields. Since the resulting objects are continuous quantities in space, a discretization scheme based on the Method of Moments (MoM) is introduced for numerical treatment. The MoM is applied in such a way, that the notation of exterior calculus is maintained while we still arrive at the same set of algebraic equations as obtained for the case of formulating the theory using the traditional notation of vector calculus. We conclude with an analytic calculation of the radiated electric field of two Hertzian dipole, excited by uncorrelated random currents.

  19. Persistence and extinction for a stochastic logistic model with infinite delay

    OpenAIRE

    Chun Lu; Xiaohua Ding

    2013-01-01

    This article, studies a stochastic logistic model with infinite delay. Using a phase space, we establish sufficient conditions for the extinction, nonpersistence in the mean, weak persistence, and stochastic permanence. A threshold between weak persistence and extinction is obtained. Our results state that different types of environmental noises have different effects on the persistence and extinction, and that the delay has no impact on the persistence and ext...

  20. Stochastic modelling of conjugate heat transfer in near-wall turbulence

    International Nuclear Information System (INIS)

    Pozorski, Jacek; Minier, Jean-Pierre

    2006-01-01

    The paper addresses the conjugate heat transfer in turbulent flows with temperature assumed to be a passive scalar. The Lagrangian approach is applied and the heat transfer is modelled with the use of stochastic particles. The intensity of thermal fluctuations in near-wall turbulence is determined from the scalar probability density function (PDF) with externally provided dynamical statistics. A stochastic model for the temperature field in the wall material is proposed and boundary conditions for stochastic particles at the solid-fluid interface are formulated. The heated channel flow with finite-thickness walls is considered as a validation case. Computation results for the mean temperature profiles and the variance of thermal fluctuations are presented and compared with available DNS data

  1. Stochastic modelling of conjugate heat transfer in near-wall turbulence

    Energy Technology Data Exchange (ETDEWEB)

    Pozorski, Jacek [Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80952 Gdansk (Poland)]. E-mail: jp@imp.gda.pl; Minier, Jean-Pierre [Research and Development Division, Electricite de France, 6 quai Watier, 78400 Chatou (France)

    2006-10-15

    The paper addresses the conjugate heat transfer in turbulent flows with temperature assumed to be a passive scalar. The Lagrangian approach is applied and the heat transfer is modelled with the use of stochastic particles. The intensity of thermal fluctuations in near-wall turbulence is determined from the scalar probability density function (PDF) with externally provided dynamical statistics. A stochastic model for the temperature field in the wall material is proposed and boundary conditions for stochastic particles at the solid-fluid interface are formulated. The heated channel flow with finite-thickness walls is considered as a validation case. Computation results for the mean temperature profiles and the variance of thermal fluctuations are presented and compared with available DNS data.

  2. Whole-field visual motion drives swimming in larval zebrafish via a stochastic process.

    Science.gov (United States)

    Portugues, Ruben; Haesemeyer, Martin; Blum, Mirella L; Engert, Florian

    2015-05-01

    Caudo-rostral whole-field visual motion elicits forward locomotion in many organisms, including larval zebrafish. Here, we investigate the dependence on the latency to initiate this forward swimming as a function of the speed of the visual motion. We show that latency is highly dependent on speed for slow speeds (1.5 s, which is much longer than neuronal transduction processes. What mechanisms underlie these long latencies? We propose two alternative, biologically inspired models that could account for this latency to initiate swimming: an integrate and fire model, which is history dependent, and a stochastic Poisson model, which has no history dependence. We use these models to predict the behavior of larvae when presented with whole-field motion of varying speed and find that the stochastic process shows better agreement with the experimental data. Finally, we discuss possible neuronal implementations of these models. © 2015. Published by The Company of Biologists Ltd.

  3. Stochastic description of heterogeneities of permeability within groundwater flow models

    International Nuclear Information System (INIS)

    Cacas, M.C.; Lachassagne, P.; Ledoux, E.; Marsily, G. de

    1991-01-01

    In order to model radionuclide migration in the geosphere realistically at the field scale, the hydrogeologist needs to be able to simulate groundwater flow in heterogeneous media. Heterogeneity of the medium can be described using a stochastic approach, that affects the way in which a flow model is formulated. In this paper, we discuss the problems that we have encountered in modelling both continuous and fractured media. The stochastic approach leads to a methodology that enables local measurements of permeability to be integrated into a model which gives a good prediction of groundwater flow on a regional scale. 5 Figs.; 8 Refs

  4. Phase field modeling of rapid crystallization in the phase-change material AIST

    Science.gov (United States)

    Tabatabaei, Fatemeh; Boussinot, Guillaume; Spatschek, Robert; Brener, Efim A.; Apel, Markus

    2017-07-01

    We carry out phase field modeling as a continuum simulation technique in order to study rapid crystallization processes in the phase-change material AIST (Ag4In3Sb67Te26). In particular, we simulate the spatio-temporal evolution of the crystallization of a molten area of the phase-change material embedded in a layer stack. The simulation model is adapted to the experimental conditions used for recent measurements of crystallization rates by a laser pulse technique. Simulations are performed for substrate temperatures close to the melting temperature of AIST down to low temperatures when an amorphous state is involved. The design of the phase field model using the thin interface limit allows us to retrieve the two limiting regimes of interface controlled (low temperatures) and thermal transport controlled (high temperatures) dynamics. Our simulations show that, generically, the crystallization velocity presents a maximum in the intermediate regime where both the interface mobility and the thermal transport, through the molten area as well as through the layer stack, are important. Simulations reveal the complex interplay of all different contributions. This suggests that the maximum switching velocity depends not only on material properties but also on the precise design of the thin film structure into which the phase-change material is embedded.

  5. Stochastic mean-field theory: Method and application to the disordered Bose-Hubbard model at finite temperature and speckle disorder

    International Nuclear Information System (INIS)

    Bissbort, Ulf; Hofstetter, Walter; Thomale, Ronny

    2010-01-01

    We discuss the stochastic mean-field theory (SMFT) method, which is a new approach for describing disordered Bose systems in the thermodynamic limit including localization and dimensional effects. We explicate the method in detail and apply it to the disordered Bose-Hubbard model at finite temperature, with on-site box disorder, as well as experimentally relevant unbounded speckle disorder. We find that disorder-induced condensation and re-entrant behavior at constant filling are only possible at low temperatures, beyond the reach of current experiments [M. Pasienski, D. McKay, M. White, and B. DeMarco, e-print arXiv:0908.1182]. Including off-diagonal hopping disorder as well, we investigate its effect on the phase diagram in addition to pure on-site disorder. To make connection to present experiments on a quantitative level, we also combine SMFT with an LDA approach and obtain the condensate fraction in the presence of an external trapping potential.

  6. INTRAVAL Phase 2: Modeling testing at the Las Cruces Trench Site

    International Nuclear Information System (INIS)

    Hills, R.G.; Rockhold, M.; Xiang, J.; Scanlon, B.; Wittmeyer, G.

    1994-01-01

    Several field experiments have been performed by scientists from the University of Arizona and New Mexico State University at the Las Cruces Trench Site to provide data tc test deterministic and stochastic models for water flow and solute transport. These experiments were performed in collaboration with INTRAVAL, an international effort toward validation of geosphere models for the transport of radionuclides. During Phase I of INTRAVAL, qualitative comparisons between experimental data and model predictions were made using contour plots of water contents and solute concentrations. Detailed quantitative comparisons were not made. To provide data for more rigorous model testing, a third Las Cruces Trench experiment was designed by scientists from the University of Arizona and New Mexico State University. Modelers from the Center for Nuclear Waste Regulatory Analysis, Massachusetts Institute of Technology, New Mexico State University, Pacific Northwest Laboratory, and the University of Texas provided predictions of water flow and tritium transport to New Mexico State University for analysis. The corresponding models assumed soil characterizations ranging from uniform to deterministically heterogeneous to stochastic. This report presents detailed quantitative comparisons to field data

  7. Stochastic generation of explicit pore structures by thresholding Gaussian random fields

    Energy Technology Data Exchange (ETDEWEB)

    Hyman, Jeffrey D., E-mail: jhyman@lanl.gov [Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721-0089 (United States); Computational Earth Science, Earth and Environmental Sciences (EES-16), and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87544 (United States); Winter, C. Larrabee, E-mail: winter@email.arizona.edu [Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721-0089 (United States); Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721-0011 (United States)

    2014-11-15

    We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1]) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone. -- Graphical abstract: -- Highlights: •An efficient method to stochastically generate realistic pore structures is provided. •Samples are generated by applying a level threshold to a Gaussian field realization. •Two user prescribed quantities determine the topology and geometry of the pore space. •Multiple pore structures and preferential flow directions can be produced. •A pore space based on Berea sandstone is generated.

  8. Mean Field Games for Stochastic Growth with Relative Utility

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Minyi, E-mail: mhuang@math.carleton.ca [Carleton University, School of Mathematics and Statistics (Canada); Nguyen, Son Luu, E-mail: sonluu.nguyen@upr.edu [University of Puerto Rico, Department of Mathematics (United States)

    2016-12-15

    This paper considers continuous time stochastic growth-consumption optimization in a mean field game setting. The individual capital stock evolution is determined by a Cobb–Douglas production function, consumption and stochastic depreciation. The individual utility functional combines an own utility and a relative utility with respect to the population. The use of the relative utility reflects human psychology, leading to a natural pattern of mean field interaction. The fixed point equation of the mean field game is derived with the aid of some ordinary differential equations. Due to the relative utility interaction, our performance analysis depends on some ratio based approximation error estimate.

  9. Mean Field Games for Stochastic Growth with Relative Utility

    International Nuclear Information System (INIS)

    Huang, Minyi; Nguyen, Son Luu

    2016-01-01

    This paper considers continuous time stochastic growth-consumption optimization in a mean field game setting. The individual capital stock evolution is determined by a Cobb–Douglas production function, consumption and stochastic depreciation. The individual utility functional combines an own utility and a relative utility with respect to the population. The use of the relative utility reflects human psychology, leading to a natural pattern of mean field interaction. The fixed point equation of the mean field game is derived with the aid of some ordinary differential equations. Due to the relative utility interaction, our performance analysis depends on some ratio based approximation error estimate.

  10. Differential form representation of stochastic electromagnetic fields

    Directory of Open Access Journals (Sweden)

    M. Haider

    2017-09-01

    Full Text Available In this work, we revisit the theory of stochastic electromagnetic fields using exterior differential forms. We present a short overview as well as a brief introduction to the application of differential forms in electromagnetic theory. Within the framework of exterior calculus we derive equations for the second order moments, describing stochastic electromagnetic fields. Since the resulting objects are continuous quantities in space, a discretization scheme based on the Method of Moments (MoM is introduced for numerical treatment. The MoM is applied in such a way, that the notation of exterior calculus is maintained while we still arrive at the same set of algebraic equations as obtained for the case of formulating the theory using the traditional notation of vector calculus. We conclude with an analytic calculation of the radiated electric field of two Hertzian dipole, excited by uncorrelated random currents.

  11. Computational stochastic model of ions implantation

    Energy Technology Data Exchange (ETDEWEB)

    Zmievskaya, Galina I., E-mail: zmi@gmail.ru; Bondareva, Anna L., E-mail: bal310775@yandex.ru [M.V. Keldysh Institute of Applied Mathematics RAS, 4,Miusskaya sq., 125047 Moscow (Russian Federation); Levchenko, Tatiana V., E-mail: tatlevchenko@mail.ru [VNII Geosystem Russian Federal Center, Varshavskoye roadway, 8, Moscow (Russian Federation); Maino, Giuseppe, E-mail: giuseppe.maino@enea.it [Scuola di Lettere e BeniCulturali, University di Bologna, sede di Ravenna, via Mariani 5, 48100 Ravenna (Italy)

    2015-03-10

    Implantation flux ions into crystal leads to phase transition /PT/ 1-st kind. Damaging lattice is associated with processes clustering vacancies and gaseous bubbles as well their brownian motion. System of stochastic differential equations /SDEs/ Ito for evolution stochastic dynamical variables corresponds to the superposition Wiener processes. The kinetic equations in partial derivatives /KE/, Kolmogorov-Feller and Einstein-Smolukhovskii, were formulated for nucleation into lattice of weakly soluble gases. According theory, coefficients of stochastic and kinetic equations uniquely related. Radiation stimulated phase transition are characterized by kinetic distribution functions /DFs/ of implanted clusters versus their sizes and depth of gas penetration into lattice. Macroscopic parameters of kinetics such as the porosity and stress calculated in thin layers metal/dielectric due to Xe{sup ++} irradiation are attracted as example. Predictions of porosity, important for validation accumulation stresses in surfaces, can be applied at restoring of objects the cultural heritage.

  12. Dynamic phase transitions and dynamic phase diagrams of the Ising model on the Shastry-Sutherland lattice

    Energy Technology Data Exchange (ETDEWEB)

    Deviren, Şeyma Akkaya, E-mail: sadeviren@nevsehir.edu.tr [Department of Science Education, Education Faculty, Nevsehir Hacı Bektaş Veli University, 50300 Nevşehir (Turkey); Deviren, Bayram [Department of Physics, Nevsehir Hacı Bektaş Veli University, 50300 Nevsehir (Turkey)

    2016-03-15

    The dynamic phase transitions and dynamic phase diagrams are studied, within a mean-field approach, in the kinetic Ising model on the Shastry-Sutherland lattice under the presence of a time varying (sinusoidal) magnetic field by using the Glauber-type stochastic dynamics. The time-dependence behavior of order parameters and the behavior of average order parameters in a period, which is also called the dynamic order parameters, as a function of temperature, are investigated. Temperature dependence of the dynamic magnetizations, hysteresis loop areas and correlations are investigated in order to characterize the nature (first- or second-order) of the dynamic phase transitions as well as to obtain the dynamic phase transition temperatures. We present the dynamic phase diagrams in the magnetic field amplitude and temperature plane. The phase diagrams exhibit a dynamic tricritical point and reentrant phenomena. The phase diagrams also contain paramagnetic (P), Néel (N), Collinear (C) phases, two coexistence or mixed regions, (N+C) and (N+P), which strongly depend on interaction parameters. - Highlights: • Dynamic magnetization properties of spin-1/2 Ising model on SSL are investigated. • Dynamic magnetization, hysteresis loop area, and correlation have been calculated. • The dynamic phase diagrams are constructed in (T/|J|, h/|J|) plane. • The phase diagrams exhibit a dynamic tricritical point and reentrant phenomena.

  13. Stochastic models in risk theory and management accounting

    NARCIS (Netherlands)

    Brekelmans, R.C.M.

    2000-01-01

    This thesis deals with stochastic models in two fields: risk theory and management accounting. Firstly, two extensions of the classical risk process are analyzed. A method is developed that computes bounds of the probability of ruin for the classical risk rocess extended with a constant interest

  14. Stochastic forecasting of the geomagnetic field from the COV-OBS.x1 geomagnetic field model, and candidate models for IGRF-12

    DEFF Research Database (Denmark)

    Gillet, Nicolas; Barrois, Olivier; Finlay, Chris

    2015-01-01

    the ‘observations’ uncertainties in data assimilation schemes for the study of the outer core dynamics.We also present and illustrate a stochastic algorithm designed to forecast the geomagnetic field. The radial field at the outer core surface is advected by core motions governed by an auto-regressive process...... filter algorithm. We show that the envelope of forecasts includes the observed secular variation of the geomagnetic field over 5-year intervals, even in the case of rapid changes. In a purpose of testing hypotheses about the core dynamics, this prototype method could be implemented to build the ‘state...

  15. Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte

    2010-01-01

    This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements....... The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a crosscorrelation...... structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared...

  16. Numerical approach of multi-field two-phase flow models in the OVAP code

    International Nuclear Information System (INIS)

    Anela Kumbaro

    2005-01-01

    Full text of publication follows: A significant progress has been made in modeling the complexity of vapor-liquid two-phase flow. Different three-dimensional models exist in order to simulate the evolution of parameters which characterize a two-phase model. These models can be classified into various groups depending on the inter-field coupling. A hierarchy of increasing physical complexity can be defined. The simplest group corresponds to the homogeneous mixture models where no interactions are taken into account. Another group is constituted by the two-fluid models employing physically important interfacial forces between two-phases, liquid, and water. The last group is multi-field modeling where inter-field couplings can be taken into account at different degrees, such as the MUltiple Size Group modeling [2], the consideration of separate equations for the transport and generation of mass and momentum for each field under the assumption of the same energy for all the fields of the same phase, and a full multi-field two-phase model [1]. The numerical approach of the general three-dimensional two-phase flow is by complexity of the phenomena a very challenging task; the ideal numerical method should be at the same time simple in order to apply to any model, from equilibrium to multi-field model and conservative in order to respect the fundamental conservation physical laws. The approximate Riemann solvers have the good properties of conservation of mass, momentum and energy balance and have been extended successfully to two-fluid models [3]- [5]. But, the up-winding of the flux is based on the Eigen-decomposition of the two-phase flow model and the computation of the Eigen-structure of a multi-field model can be a high cost procedure. Our contribution will present a short review of the above two-phase models, and show numerical results obtained for some of them with an approximate Riemann solver and with lower-complexity alternative numerical methods that do not

  17. Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

    International Nuclear Information System (INIS)

    Cruz, Roberto de la; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

    2017-01-01

    The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction–diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction–diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge

  18. Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth

    Science.gov (United States)

    de la Cruz, Roberto; Guerrero, Pilar; Calvo, Juan; Alarcón, Tomás

    2017-12-01

    The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction-diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction-diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of

  19. Stochastic quantisation: theme and variation

    International Nuclear Information System (INIS)

    Klauder, J.R.; Kyoto Univ.

    1987-01-01

    The paper on stochastic quantisation is a contribution to the book commemorating the sixtieth birthday of E.S. Fradkin. Stochastic quantisation reformulates Euclidean quantum field theory in the language of Langevin equations. The generalised free field is discussed from the viewpoint of stochastic quantisation. An artificial family of highly singular model theories wherein the space-time derivatives are dropped altogether is also examined. Finally a modified form of stochastic quantisation is considered. (U.K.)

  20. A phase transition between small- and large-field models of inflation

    International Nuclear Information System (INIS)

    Itzhaki, Nissan; Kovetz, Ely D

    2009-01-01

    We show that models of inflection point inflation exhibit a phase transition from a region in parameter space where they are of large-field type to a region where they are of small-field type. The phase transition is between a universal behavior, with respect to the initial condition, at the large-field region and non-universal behavior at the small-field region. The order parameter is the number of e-foldings. We find integer critical exponents at the transition between the two phases.

  1. Revisiting the cape cod bacteria injection experiment using a stochastic modeling approach

    Science.gov (United States)

    Maxwell, R.M.; Welty, C.; Harvey, R.W.

    2007-01-01

    Bromide and resting-cell bacteria tracer tests conducted in a sandy aquifer at the U.S. Geological Survey Cape Cod site in 1987 were reinterpreted using a three-dimensional stochastic approach. Bacteria transport was coupled to colloid filtration theory through functional dependence of local-scale colloid transport parameters upon hydraulic conductivity and seepage velocity in a stochastic advection - dispersion/attachment - detachment model. Geostatistical information on the hydraulic conductivity (K) field that was unavailable at the time of the original test was utilized as input. Using geostatistical parameters, a groundwater flow and particle-tracking model of conservative solute transport was calibrated to the bromide-tracer breakthrough data. An optimization routine was employed over 100 realizations to adjust the mean and variance ofthe natural-logarithm of hydraulic conductivity (InK) field to achieve best fit of a simulated, average bromide breakthrough curve. A stochastic particle-tracking model for the bacteria was run without adjustments to the local-scale colloid transport parameters. Good predictions of mean bacteria breakthrough were achieved using several approaches for modeling components of the system. Simulations incorporating the recent Tufenkji and Elimelech (Environ. Sci. Technol. 2004, 38, 529-536) correlation equation for estimating single collector efficiency were compared to those using the older Rajagopalan and Tien (AIChE J. 1976, 22, 523-533) model. Both appeared to work equally well at predicting mean bacteria breakthrough using a constant mean bacteria diameter for this set of field conditions. Simulations using a distribution of bacterial cell diameters available from original field notes yielded a slight improvement in the model and data agreement compared to simulations using an average bacterial diameter. The stochastic approach based on estimates of local-scale parameters for the bacteria-transport process reasonably captured

  2. Stochastic models of solute transport in highly heterogeneous geologic media

    Energy Technology Data Exchange (ETDEWEB)

    Semenov, V.N.; Korotkin, I.A.; Pruess, K.; Goloviznin, V.M.; Sorokovikova, O.S.

    2009-09-15

    A stochastic model of anomalous diffusion was developed in which transport occurs by random motion of Brownian particles, described by distribution functions of random displacements with heavy (power-law) tails. One variant of an effective algorithm for random function generation with a power-law asymptotic and arbitrary factor of asymmetry is proposed that is based on the Gnedenko-Levy limit theorem and makes it possible to reproduce all known Levy {alpha}-stable fractal processes. A two-dimensional stochastic random walk algorithm has been developed that approximates anomalous diffusion with streamline-dependent and space-dependent parameters. The motivation for introducing such a type of dispersion model is the observed fact that tracers in natural aquifers spread at different super-Fickian rates in different directions. For this and other important cases, stochastic random walk models are the only known way to solve the so-called multiscaling fractional order diffusion equation with space-dependent parameters. Some comparisons of model results and field experiments are presented.

  3. A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors

    Directory of Open Access Journals (Sweden)

    Spiros Pagiatakis

    2009-10-01

    Full Text Available In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times. It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF. It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at −40 °C, −20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

  4. A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

    Science.gov (United States)

    El-Diasty, Mohammed; Pagiatakis, Spiros

    2009-01-01

    In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at -40 °C, -20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

  5. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    Science.gov (United States)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-06-01

    -dimensional input stochastic models to represent thermal diffusivity in two-phase microstructures. This model is used in analyzing the effect of topological variations of two-phase microstructures on the evolution of temperature in heat conduction processes.

  6. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    International Nuclear Information System (INIS)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-01-01

    by constructing low-dimensional input stochastic models to represent thermal diffusivity in two-phase microstructures. This model is used in analyzing the effect of topological variations of two-phase microstructures on the evolution of temperature in heat conduction processes

  7. Stochastic modeling concepts in groundwater and risk assessment: potential application to marine problems

    International Nuclear Information System (INIS)

    Hamed, Maged M.

    2000-01-01

    Parameter uncertainty is ubiquitous in marine environmental processes. Failure to account for this uncertainty may lead to erroneous results, and may have significant environmental and economic ramifications. Stochastic modeling of oil spill transport and fate is, therefore, central in the development of an oil spill contingency plan for new oil and gas projects. Over the past twenty years, several stochastic modeling tools have been developed for modeling parameter uncertainty, including the spectral, perturbation, and simulation methods. In this work we explore the application of a new stochastic methodology, the first-order reliability method (FORM), in oil spill modeling. FORM was originally developed in the structural reliability field and has been recently applied to various environmental problems. The method has many appealing features that makes it a powerful tool for modeling complex environmental systems. The theory of FORM is presented, identifying the features that distinguish the method from other stochastic tools. Different formulations to the reliability-based stochastic oil spill modeling are presented in a decision-analytic context. (Author)

  8. Explicit calibration and simulation of stochastic fields by low-order ARMA processes

    DEFF Research Database (Denmark)

    Krenk, Steen

    2011-01-01

    A simple framework for autoregressive simulation of stochastic fields is presented. The autoregressive format leads to a simple exponential correlation structure in the time-dimension. In the case of scalar processes a more detailed correlation structure can be obtained by adding memory...... to the process via an extension to autoregressive moving average (ARMA) processes. The ARMA format incorporates a more detailed correlation structure by including previous values of the simulated process. Alternatively, a more detailed correlation structure can be obtained by including additional 'state......-space' variables in the simulation. For a scalar process this would imply an increase of the dimension of the process to be simulated. In the case of a stochastic field the correlation in the time-dimension is represented, although indirectly, in the simultaneous spatial correlation. The model with the shortest...

  9. Endogenous fields enhanced stochastic resonance in a randomly coupled neuronal network

    International Nuclear Information System (INIS)

    Deng, Bin; Wang, Lin; Wang, Jiang; Wei, Xi-le; Yu, Hai-tao

    2014-01-01

    Highlights: • We study effects of endogenous fields on stochastic resonance in a neural network. • Stochastic resonance can be notably enhanced by endogenous field feedback. • Endogenous field feedback delay plays a vital role in stochastic resonance. • The parameters of low-passed filter play a subtle role in SR. - Abstract: Endogenous field, evoked by structured neuronal network activity in vivo, is correlated with many vital neuronal processes. In this paper, the effects of endogenous fields on stochastic resonance (SR) in a randomly connected neuronal network are investigated. The network consists of excitatory and inhibitory neurons and the axonal conduction delays between neurons are also considered. Numerical results elucidate that endogenous field feedback results in more rhythmic macroscope activation of the network for proper time delay and feedback coefficient. The response of the network to the weak periodic stimulation can be notably enhanced by endogenous field feedback. Moreover, the endogenous field feedback delay plays a vital role in SR. We reveal that appropriately tuned delays of the feedback can either induce the enhancement of SR, appearing at every integer multiple of the weak input signal’s oscillation period, or the depression of SR, appearing at every integer multiple of half the weak input signal’s oscillation period for the same feedback coefficient. Interestingly, the parameters of low-passed filter which is used in obtaining the endogenous field feedback signal play a subtle role in SR

  10. Stochastic diffusion models for substitutable technological innovations

    NARCIS (Netherlands)

    Wang, L.; Hu, B.; Yu, X.

    2004-01-01

    Based on the analysis of firms' stochastic adoption behaviour, this paper first points out the necessity to build more practical stochastic models. And then, stochastic evolutionary models are built for substitutable innovation diffusion system. Finally, through the computer simulation of the

  11. Stochastic quantization and gauge-fixing of the linearized gravitational field

    International Nuclear Information System (INIS)

    Hueffel, H.; Rumpf, H.

    1984-01-01

    Due to the indefiniteness of the Euclidean gravitational action the Parisi-Wu stochastic quantization scheme fails in the case of the gravitational field. Therefore we apply a recently proposed modification of stochastic quantization that works in Minkowski space and preserves all the advantages of the original Parisi-Wu method; in particular no gauge-fixing is required. Additionally stochastic gauge-fixing may be introduced and is also studied in detail. The graviton propagators obtained with and without stochastic gauge-fixing all exhibit a noncausal contribution, but apart from this effect the gauge-invariant quantities are the same as those of standard quantization. (Author)

  12. Stochastic series expansion simulation of the t -V model

    Science.gov (United States)

    Wang, Lei; Liu, Ye-Hua; Troyer, Matthias

    2016-04-01

    We present an algorithm for the efficient simulation of the half-filled spinless t -V model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size, and is free from the time-discretization error. We use it to map out the finite-temperature phase diagram of the spinless t -V model on the honeycomb lattice and observe a suppression of the critical temperature of the charge-density-wave phase in the vicinity of a fermionic quantum critical point.

  13. A stochastic-field description of finite-size spiking neural networks.

    Science.gov (United States)

    Dumont, Grégory; Payeur, Alexandre; Longtin, André

    2017-08-01

    Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time. The population activity-the density of active neurons per unit time-is easily extracted from this refractory density. The SPDE includes finite-size effects through a two-dimensional Gaussian white noise that acts both in time and along the refractory dimension. For an infinite number of neurons the standard mean-field theory is recovered. A discretization of the SPDE along its characteristic curves allows direct simulations of the activity of large but finite spiking networks; this constitutes the main advantage of our approach. Linearizing the SPDE with respect to the deterministic asynchronous state allows the theoretical investigation of finite-size activity fluctuations. In particular, analytical expressions for the power spectrum and autocorrelation of activity fluctuations are obtained. Moreover, our approach can be adapted to incorporate multiple interacting populations and quasi-renewal single-neuron dynamics.

  14. Diffusion of charged particles in a stochastic magnetic field

    International Nuclear Information System (INIS)

    Balescu, R.; Misguich, J.H.; Nakach, R.

    1992-07-01

    The diffusive motion of charged particles in a stochastic magnetic field is investigated systematically in a model in which the statistics of both the collisions and the magnetic field are described by coloured noises characterized, respectively, by a finite correlation time and finite correlation lengths. An analytic solution is obtained for the basic nonlinear differential equation of the model..It describes asymptotically a pure diffusion process, in which the mean square displacement in the perpendicular direction, Γ(t), grows proportionally to time (after a sufficiently long time). The corresponding diffusion coefficient scales like the fourth power of the magnetic fluctuation intensity. The values obtained are in very good agreement with experimental data in reverse-field pinch experiments. The present result contradicts earlier results predicting subdiffusive behaviour: Γ(t) ∼ t 1/2 or Γ(t) ∼ t 1/4 . The relation of these results to ours is discussed in detail

  15. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  16. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  17. Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms

    DEFF Research Database (Denmark)

    Marbjerg, Gerd Høy; Brunskog, Jonas; Jeong, Cheol-Ho

    2015-01-01

    A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse...... radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber...

  18. Electron transport in the stochastic fields of the reversed-field pinch

    International Nuclear Information System (INIS)

    Kim, M.-H.; Punjabi, A.

    1996-01-01

    We employ the Monte Carlo method for the calculation of anomalous transport developed by Punjabi and Boozer to calculate the particle diffusion coefficient for electrons in the stochastic magnetic fields of the reversed-field pinch (RFP). In the Monte Carlo calculations represented here, the transport mechanism is the loss of magnetic surfaces due to resistive perturbations. The equilibrium magnetic fields are represented by the Bessel function model for the RFP. The diffusion coefficient D is calculated as a function of a, the amplitude of the perturbation. We see three regimes as the amplitude of the tearing modes is increased: the Rechester-Rosenbluth regime where D scales as a 2 ; the anomalous regime where D scales more rapidly than a 2 ; and the Mynick-Krommes regime where D scales more slowly than a 2 . Inclusion of the effects of loop voltage on the particle drift orbits in the RFP does not affect the intervals in the amplitude a where these regimes operate. (Author)

  19. Numerical Simulation of the Heston Model under Stochastic Correlation

    Directory of Open Access Journals (Sweden)

    Long Teng

    2017-12-01

    Full Text Available Stochastic correlation models have become increasingly important in financial markets. In order to be able to price vanilla options in stochastic volatility and correlation models, in this work, we study the extension of the Heston model by imposing stochastic correlations driven by a stochastic differential equation. We discuss the efficient algorithms for the extended Heston model by incorporating stochastic correlations. Our numerical experiments show that the proposed algorithms can efficiently provide highly accurate results for the extended Heston by including stochastic correlations. By investigating the effect of stochastic correlations on the implied volatility, we find that the performance of the Heston model can be proved by including stochastic correlations.

  20. Front Propagation in Stochastic Neural Fields

    KAUST Repository

    Bressloff, Paul C.

    2012-01-01

    We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement (wandering) of the front from its uniformly translating position at long time scales, and fluctuations in the front profile around its instantaneous position at short time scales. One major result of our analysis is a comparison between freely propagating fronts and fronts locked to an externally moving stimulus. We show that the latter are much more robust to noise, since the stochastic wandering of the mean front profile is described by an Ornstein-Uhlenbeck process rather than a Wiener process, so that the variance in front position saturates in the long time limit rather than increasing linearly with time. Finally, we consider a stochastic neural field that supports a pulled front in the deterministic limit, and show that the wandering of such a front is now subdiffusive. © 2012 Society for Industrial and Applied Mathematics.

  1. Diffusion of test particles in stochastic magnetic fields for small Kubo numbers

    International Nuclear Information System (INIS)

    Neuer, Marcus; Spatschek, Karl H.

    2006-01-01

    Motion of charged particles in a collisional plasma with stochastic magnetic field lines is investigated on the basis of the so-called A-Langevin equation. Compared to the previously used V-Langevin model, here finite Larmor radius effects are taken into account. The A-Langevin equation is solved under the assumption that the Lagrangian correlation function for the magnetic field fluctuations is related to the Eulerian correlation function (in Gaussian form) via the Corrsin approximation. The latter is justified for small Kubo numbers. The velocity correlation function, being averaged with respect to the stochastic variables including collisions, leads to an implicit differential equation for the mean square displacement. From the latter, different transport regimes, including the well-known Rechester-Rosenbluth diffusion coefficient, are derived. Finite Larmor radius contributions show a decrease of the diffusion coefficient compared to the guiding center limit. The case of small (or vanishing) mean fields is also discussed

  2. Quantum dynamical time evolutions as stochastic flows on phase space

    International Nuclear Information System (INIS)

    Combe, P.; Rodriguez, R.; Guerra, F.; Sirigue, M.; Sirigue-Collin, M.

    1984-01-01

    We are mainly interested in describing the time development of the Wigner functions by means of stochastic processes. In the second section we recall the main properties of the Wigner functions as well as those of their Fourier transform. In the next one we derive the evolution equation of these functions for a class of Hamiltonians and we give a probabilistic expression for the solution of these equations by means of a stochastic flow in phase space which reminds of the classical flows. In the last section we remark that the previously defined flow can be extended to the bounded continuous functions on phase space and that this flow conserves the cone generated by the Wigner functions. (orig./HSI)

  3. Stochastic dynamics for reinfection by transmitted diseases

    Science.gov (United States)

    Barros, Alessandro S.; Pinho, Suani T. R.

    2017-06-01

    The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous reinfection due to direct contact with an infected individual (with smaller reinfection rate σ β than infection rate β ). In this paper, we examine the stochastic susceptible, infected, recovered, infected (SIRI) model simulating the endogenous reactivation by a spontaneous reaction, while exogenous reinfection by a catalytic reaction. Analyzing the mean-field approximations of a site and pairs of sites, and Monte Carlo (MC) simulations for the particular case of exogenous reinfection, we obtained continuous phase transitions involving endemic, epidemic, and no transmission phases for the simple approach; the approach of pairs is better to describe the phase transition from endemic phase (susceptible, infected, susceptible (SIS)-like model) to epidemic phase (susceptible, infected, and removed or recovered (SIR)-like model) considering the comparison with MC results; the reinfection increases the peaks of outbreaks until the system reaches endemic phase. For the particular case of endogenous reactivation, the approach of pairs leads to a continuous phase transition from endemic phase (SIS-like model) to no transmission phase. Finally, there is no phase transition when both effects are taken into account. We hope the results of this study can be generalized for the susceptible, exposed, infected, and removed or recovered (SEIRIE) model, for which the state exposed (infected but not infectious), describing more realistically transmitted diseases such as tuberculosis. In future work, we also intend to investigate the effect of network topology on phase transitions when the SIRI model describes both transmitted diseases (σ social contagions (σ >1 ).

  4. Stochastic Finite Elements in Reliability-Based Structural Optimization

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Engelund, S.

    Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect to optimi......Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect...

  5. Optimum gain and phase for stochastic cooling systems

    International Nuclear Information System (INIS)

    Meer, S. van der.

    1984-01-01

    A detailed analysis of optimum gain and phase adjustment in stochastic cooling systems reveals that the result is strongly influenced by the beam feedback effect and that for optimum performance the system phase should change appreciably across each Schottky band. It is shown that the performance is not greatly diminished if a constant phase is adopted instead. On the other hand, the effect of mixing between pick-up and kicker (which produces a phase change similar to the optimum one) is shown to be less perturbing than is usually assumed, provided that the absolute value of the gain is not too far from the optimum value. (orig.)

  6. The extended local gauge invariance and the BRS symmetry in stochastic quantization of gauge fields

    International Nuclear Information System (INIS)

    Nakazawa, Naohito.

    1989-05-01

    We investigate the BRS invariance of the first-class constrained systems in the context of the stochastic quantization. For the first-class constrained systems, we construct the nilpotent BRS transformation and the BRS invariant stochastic effective action based on the D+1 dimensional field theoretical formulation of stochastic quantization. By eliminating the multiplier field of the gauge fixing condition and an auxiliary field, it is shown that there exists a truncated BRS transformation which satisfies the nilpotency condition. The truncated BRS invariant stochastic action is also derived. As the examples of the general formulation, we investigate the BRS invariant structure in the massless and massive Yang-Mills fields in stochastic quantization. (author)

  7. Green's Function and Stress Fields in Stochastic Heterogeneous Continua

    Science.gov (United States)

    Negi, Vineet

    Many engineering materials used today are heterogenous in composition e.g. Composites - Polymer Matrix Composites, Metal Matrix Composites. Even, conventional engineering materials - metals, plastics, alloys etc. - may develop heterogeneities, like inclusions and residual stresses, during the manufacturing process. Moreover, these materials may also have intrinsic heterogeneities at a nanoscale in the form of grain boundaries in metals, crystallinity in amorphous polymers etc. While, the homogenized constitutive models for these materials may be satisfactory at a macroscale, recent studies of phenomena like fatigue failure, void nucleation, size-dependent brittle-ductile transition in polymeric nanofibers reveal a major play of micro/nanoscale physics in these phenomena. At this scale, heterogeneities in a material may no longer be ignored. Thus, this demands a study into the effects of various material heterogeneities. In this work, spatial heterogeneities in two material properties - elastic modulus and yield stress - have been investigated separately. The heterogeneity in the elastic modulus is studied in the context of Green's function. The Stochastic Finite Element method is adopted to get the mean statistics of the Green's function defined on a stochastic heterogeneous 2D infinite space. A study of the elastic-plastic transition in a domain having stochastic heterogenous yield stress was done using Mont-Carlo methods. The statistics for various stress and strain fields during the transition were obtained. Further, the effects of size of the domain and the strain-hardening rate on the stress fields during the heterogeneous elastic-plastic transition were investigated. Finally, a case is made for the role of the heterogenous elastic-plastic transition in damage nucleation and growth.

  8. Stochastic description of cascade size effects on phase stability under irradiation

    International Nuclear Information System (INIS)

    Martin, G.; Bellon, P.

    1988-01-01

    Cascade size may affect phase stability under irradiation because of two distinct contributions: the replacement to displacement cross section ratio depends on the deposited energy density; ballistic jumps which tend to disorder ordere compounds occur by bursts (of size b), while thermal jumps which restored long range order occur one by one. The latter effect cannot be handled by standard rate theory. A stochastic treatment of the problem, based on a Fokker Planck approximation of the relevant master equation is summarized. It is shown that the possible values of the long range order parameter under irradiation are not affected by the size b of the bursts, but that the respective stability of the former is b dependent. As a consequence, the stability diagram of phases under irradiation varies with b. Such a diagram is computed for the Ni 4 Mo system where three structures are competing: the disordered solid solution, D1 a and DO 23 . A broadening by 100K of the stability domain of the short range ordered structure to the expense of the long range ordered one is predicted when increasing b from 1 to 100. The stochastic potentials introduced in the present treatment are by no means free energies of some constrained state. They can however be computed in a mean field type approximation. 23 refs

  9. Rate-independent dissipation in phase-field modelling of displacive transformations

    Science.gov (United States)

    Tůma, K.; Stupkiewicz, S.; Petryk, H.

    2018-05-01

    In this paper, rate-independent dissipation is introduced into the phase-field framework for modelling of displacive transformations, such as martensitic phase transformation and twinning. The finite-strain phase-field model developed recently by the present authors is here extended beyond the limitations of purely viscous dissipation. The variational formulation, in which the evolution problem is formulated as a constrained minimization problem for a global rate-potential, is enhanced by including a mixed-type dissipation potential that combines viscous and rate-independent contributions. Effective computational treatment of the resulting incremental problem of non-smooth optimization is developed by employing the augmented Lagrangian method. It is demonstrated that a single Lagrange multiplier field suffices to handle the dissipation potential vertex and simultaneously to enforce physical constraints on the order parameter. In this way, the initially non-smooth problem of evolution is converted into a smooth stationarity problem. The model is implemented in a finite-element code and applied to solve two- and three-dimensional boundary value problems representative for shape memory alloys.

  10. Stochastic dynamics of phase singularities under ventricular fibrillation in 2D Beeler-Reuter model

    Directory of Open Access Journals (Sweden)

    Akio Suzuki

    2011-09-01

    Full Text Available The dynamics of ventricular fibrillation (VF has been studied extensively, and the initiation mechanism of VF has been elucidated to some extent. However, the stochastic dynamical nature of sustained VF remains unclear so far due to the complexity of high dimensional chaos in a heterogeneous system. In this paper, various statistical mechanical properties of sustained VF are studied numerically in 2D Beeler-Reuter-Drouhard-Roberge (BRDR model with normal and modified ionic current conductance. The nature of sustained VF is analyzed by measuring various fluctuations of spatial phase singularity (PS such as velocity, lifetime, the rates of birth and death. It is found that the probability density function (pdf for lifetime of PSs is independent of system size. It is also found that the hyper-Gamma distribution serves as a universal pdf for the counting number of PSs for various system sizes and various parameters of our model tissue under VF. Further, it is demonstrated that the nonlinear Langevin equation associated with a hyper-Gamma process can mimic the pdf and temporal variation of the number of PSs in the 2D BRDR model.

  11. Transport properties of stochastic Lorentz models

    NARCIS (Netherlands)

    Beijeren, H. van

    Diffusion processes are considered for one-dimensional stochastic Lorentz models, consisting of randomly distributed fixed scatterers and one moving light particle. In waiting time Lorentz models the light particle makes instantaneous jumps between scatterers after a stochastically distributed

  12. Phase transition and computational complexity in a stochastic prime number generator

    Energy Technology Data Exchange (ETDEWEB)

    Lacasa, L; Luque, B [Departamento de Matematica Aplicada y EstadIstica, ETSI Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Miramontes, O [Departamento de Sistemas Complejos, Instituto de FIsica, Universidad Nacional Autonoma de Mexico, Mexico 01415 DF (Mexico)], E-mail: lucas@dmae.upm.es

    2008-02-15

    We introduce a prime number generator in the form of a stochastic algorithm. The character of this algorithm gives rise to a continuous phase transition which distinguishes a phase where the algorithm is able to reduce the whole system of numbers into primes and a phase where the system reaches a frozen state with low prime density. In this paper, we firstly present a broader characterization of this phase transition, both in analytical and numerical terms. Critical exponents are calculated, and data collapse is provided. Further on, we redefine the model as a search problem, fitting it in the hallmark of computational complexity theory. We suggest that the system belongs to the class NP. The computational cost is maximal around the threshold, as is common in many algorithmic phase transitions, revealing the presence of an easy-hard-easy pattern. We finally relate the nature of the phase transition to an average-case classification of the problem.

  13. Computation of multiphase systems with phase field models

    International Nuclear Information System (INIS)

    Badalassi, V.E.; Ceniceros, H.D.; Banerjee, S.

    2003-01-01

    Phase field models offer a systematic physical approach for investigating complex multiphase systems behaviors such as near-critical interfacial phenomena, phase separation under shear, and microstructure evolution during solidification. However, because interfaces are replaced by thin transition regions (diffuse interfaces), phase field simulations require resolution of very thin layers to capture the physics of the problems studied. This demands robust numerical methods that can efficiently achieve high resolution and accuracy, especially in three dimensions. We present here an accurate and efficient numerical method to solve the coupled Cahn-Hilliard/Navier-Stokes system, known as Model H, that constitutes a phase field model for density-matched binary fluids with variable mobility and viscosity. The numerical method is a time-split scheme that combines a novel semi-implicit discretization for the convective Cahn-Hilliard equation with an innovative application of high-resolution schemes employed for direct numerical simulations of turbulence. This new semi-implicit discretization is simple but effective since it removes the stability constraint due to the nonlinearity of the Cahn-Hilliard equation at the same cost as that of an explicit scheme. It is derived from a discretization used for diffusive problems that we further enhance to efficiently solve flow problems with variable mobility and viscosity. Moreover, we solve the Navier-Stokes equations with a robust time-discretization of the projection method that guarantees better stability properties than those for Crank-Nicolson-based projection methods. For channel geometries, the method uses a spectral discretization in the streamwise and spanwise directions and a combination of spectral and high order compact finite difference discretizations in the wall normal direction. The capabilities of the method are demonstrated with several examples including phase separation with, and without, shear in two and three

  14. Modeling and analysis of stochastic systems

    CERN Document Server

    Kulkarni, Vidyadhar G

    2011-01-01

    Based on the author's more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. Along with reorganizing the material, this edition revises and adds new exercises and examples. New to the second edi

  15. Predicting the Stochastic Properties of the Shallow Subsurface for Improved Geophysical Modeling

    Science.gov (United States)

    Stroujkova, A.; Vynne, J.; Bonner, J.; Lewkowicz, J.

    2005-12-01

    Strong ground motion data from numerous explosive field experiments and from moderate to large earthquakes show significant variations in amplitude and waveform shape with respect to both azimuth and range. Attempts to model these variations using deterministic models have often been unsuccessful. It has been hypothesized that a stochastic description of the geological medium is a more realistic approach. To estimate the stochastic properties of the shallow subsurface, we use Measurement While Drilling (MWD) data, which are routinely collected by mines in order to facilitate design of blast patterns. The parameters, such as rotation speed of the drill, torque, and penetration rate, are used to compute the rock's Specific Energy (SE), which is then related to a blastability index. We use values of SE measured at two different mines and calibrated to laboratory measurements of rock properties to determine correlation lengths of the subsurface rocks in 2D, needed to obtain 2D and 3D stochastic models. The stochastic models are then combined with the deterministic models and used to compute synthetic seismic waveforms.

  16. A phase-field model for non-equilibrium solidification of intermetallics

    International Nuclear Information System (INIS)

    Assadi, H.

    2007-01-01

    Intermetallics may exhibit unique solidification behaviour-including slow growth kinetics, anomalous partitioning and formation of unusual growth morphologies-because of departure from local equilibrium. A phase-field model is developed and used to illustrate these non-equilibrium effects in solidification of a prototype B2 intermetallic phase. The model takes sublattice compositions as primary field variables, from which chemical long-range order is derived. The diffusive reactions between the two sublattices, and those between each sublattice and the liquid phase are taken as 'internal' kinetic processes, which take place within control volumes of the system. The model can thus capture solute and disorder trapping effects, which are consistent-over a wide range of the solid/liquid interface thickness-with the predictions of the sharp-interface theory of solute and disorder trapping. The present model can also take account of solid-state ordering and thus illustrate the effects of chemical ordering on microstructure formation and crystal growth kinetics

  17. Application of Stochastic Partial Differential Equations to Reservoir Property Modelling

    KAUST Repository

    Potsepaev, R.

    2010-09-06

    Existing algorithms of geostatistics for stochastic modelling of reservoir parameters require a mapping (the \\'uvt-transform\\') into the parametric space and reconstruction of a stratigraphic co-ordinate system. The parametric space can be considered to represent a pre-deformed and pre-faulted depositional environment. Existing approximations of this mapping in many cases cause significant distortions to the correlation distances. In this work we propose a coordinate free approach for modelling stochastic textures through the application of stochastic partial differential equations. By avoiding the construction of a uvt-transform and stratigraphic coordinates, one can generate realizations directly in the physical space in the presence of deformations and faults. In particular the solution of the modified Helmholtz equation driven by Gaussian white noise is a zero mean Gaussian stationary random field with exponential correlation function (in 3-D). This equation can be used to generate realizations in parametric space. In order to sample in physical space we introduce a stochastic elliptic PDE with tensor coefficients, where the tensor is related to correlation anisotropy and its variation is physical space.

  18. Interplanetary Alfvenic fluctuations: A stochastic model

    International Nuclear Information System (INIS)

    Barnes, A.

    1981-01-01

    The strong alignment of the average directions of minimum magnetic variance and mean magnetic field in interplanetary Alfvenic fluctuations is inconsistent with the usual wave-propagation models. We investigate the concept of minimum variance for nonplanar Alfvenic fluctuations in which the field direction varies stochastically. It is found that the tendency of the minimum variance and mean field directions to be aligned may be purely a consequence of the randomness of the field direction. In particular, a well-defined direction of minimum variance does not imply that the fluctuations are necessarily planar. The fluctuation power spectrum is a power law for frequencies much higher than the inverse of the correlation time. The probability distribution of directions a randomly fluctuating field of constant magnitude is calculated. A new approach for observational studies of interplanetary fluctuations is suggested

  19. Scalar field dark matter in hybrid approach

    NARCIS (Netherlands)

    Friedrich, Pavel; Prokopec, Tomislav

    2017-01-01

    We develop a hybrid formalism suitable for modeling scalar field dark matter, in which the phase-space distribution associated to the real scalar field is modeled by statistical equal-time two-point functions and gravity is treated by two stochastic gravitational fields in the longitudinal gauge (in

  20. Classical nucleation theory in the phase-field crystal model.

    Science.gov (United States)

    Jreidini, Paul; Kocher, Gabriel; Provatas, Nikolas

    2018-04-01

    A full understanding of polycrystalline materials requires studying the process of nucleation, a thermally activated phase transition that typically occurs at atomistic scales. The numerical modeling of this process is problematic for traditional numerical techniques: commonly used phase-field methods' resolution does not extend to the atomic scales at which nucleation takes places, while atomistic methods such as molecular dynamics are incapable of scaling to the mesoscale regime where late-stage growth and structure formation takes place following earlier nucleation. Consequently, it is of interest to examine nucleation in the more recently proposed phase-field crystal (PFC) model, which attempts to bridge the atomic and mesoscale regimes in microstructure simulations. In this work, we numerically calculate homogeneous liquid-to-solid nucleation rates and incubation times in the simplest version of the PFC model, for various parameter choices. We show that the model naturally exhibits qualitative agreement with the predictions of classical nucleation theory (CNT) despite a lack of some explicit atomistic features presumed in CNT. We also examine the early appearance of lattice structure in nucleating grains, finding disagreement with some basic assumptions of CNT. We then argue that a quantitatively correct nucleation theory for the PFC model would require extending CNT to a multivariable theory.

  1. Classical nucleation theory in the phase-field crystal model

    Science.gov (United States)

    Jreidini, Paul; Kocher, Gabriel; Provatas, Nikolas

    2018-04-01

    A full understanding of polycrystalline materials requires studying the process of nucleation, a thermally activated phase transition that typically occurs at atomistic scales. The numerical modeling of this process is problematic for traditional numerical techniques: commonly used phase-field methods' resolution does not extend to the atomic scales at which nucleation takes places, while atomistic methods such as molecular dynamics are incapable of scaling to the mesoscale regime where late-stage growth and structure formation takes place following earlier nucleation. Consequently, it is of interest to examine nucleation in the more recently proposed phase-field crystal (PFC) model, which attempts to bridge the atomic and mesoscale regimes in microstructure simulations. In this work, we numerically calculate homogeneous liquid-to-solid nucleation rates and incubation times in the simplest version of the PFC model, for various parameter choices. We show that the model naturally exhibits qualitative agreement with the predictions of classical nucleation theory (CNT) despite a lack of some explicit atomistic features presumed in CNT. We also examine the early appearance of lattice structure in nucleating grains, finding disagreement with some basic assumptions of CNT. We then argue that a quantitatively correct nucleation theory for the PFC model would require extending CNT to a multivariable theory.

  2. Phase Structure Of Fuzzy Field Theories And Multi trace Matrix Models

    International Nuclear Information System (INIS)

    Tekel, J.

    2015-01-01

    We review the interplay of fuzzy field theories and matrix models, with an emphasis on the phase structure of fuzzy scalar field theories. We give a self-contained introduction to these topics and give the details concerning the saddle point approach for the usual single trace and multi trace matrix models. We then review the attempts to explain the phase structure of the fuzzy field theory using a corresponding random matrix ensemble, showing the strength and weaknesses of this approach. We conclude with a list of challenges one needs to overcome and the most interesting open problems one can try to solve. (author)

  3. Modelling Cow Behaviour Using Stochastic Automata

    DEFF Research Database (Denmark)

    Jónsson, Ragnar Ingi

    This report covers an initial study on the modelling of cow behaviour using stochastic automata with the aim of detecting lameness. Lameness in cows is a serious problem that needs to be dealt with because it results in less profitable production units and in reduced quality of life...... for the affected livestock. By featuring training data consisting of measurements of cow activity, three different models are obtained, namely an autonomous stochastic automaton, a stochastic automaton with coinciding state and output and an autonomous stochastic automaton with coinciding state and output, all...... of which describe the cows' activity in the two regarded behavioural scenarios, non-lame and lame. Using the experimental measurement data the different behavioural relations for the two regarded behavioural scenarios are assessed. The three models comprise activity within last hour, activity within last...

  4. Improved ensemble-mean forecast skills of ENSO events by a zero-mean stochastic model-error model of an intermediate coupled model

    Science.gov (United States)

    Zheng, F.; Zhu, J.

    2015-12-01

    To perform an ensemble-based ENSO probabilistic forecast, the crucial issue is to design a reliable ensemble prediction strategy that should include the major uncertainties of a forecast system. In this study, we developed a new general ensemble perturbation technique to improve the ensemble-mean predictive skill of forecasting ENSO using an intermediate coupled model (ICM). The model uncertainties are first estimated and analyzed from EnKF analysis results through assimilating observed SST. Then, based on the pre-analyzed properties of the model errors, a zero-mean stochastic model-error model is developed to mainly represent the model uncertainties induced by some important physical processes missed in the coupled model (i.e., stochastic atmospheric forcing/MJO, extra-tropical cooling and warming, Indian Ocean Dipole mode, etc.). Each member of an ensemble forecast is perturbed by the stochastic model-error model at each step during the 12-month forecast process, and the stochastical perturbations are added into the modeled physical fields to mimic the presence of these high-frequency stochastic noises and model biases and their effect on the predictability of the coupled system. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr retrospective forecast experiments. The two forecast schemes are differentiated by whether they considered the model stochastic perturbations, with both initialized by the ensemble-mean analysis states from EnKF. The comparison results suggest that the stochastic model-error perturbations have significant and positive impacts on improving the ensemble-mean prediction skills during the entire 12-month forecast process. Because the nonlinear feature of the coupled model can induce the nonlinear growth of the added stochastic model errors with model integration, especially through the nonlinear heating mechanism with the vertical advection term of the model, the

  5. Electricity price modeling with stochastic time change

    International Nuclear Information System (INIS)

    Borovkova, Svetlana; Schmeck, Maren Diane

    2017-01-01

    In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally occurring price spikes) into the model in an elegant and economically justifiable way. The stochastic time change introduces stochastic as well as deterministic (e.g., seasonal) features in the price process' volatility and in the jump component. We specify the base process as a mean reverting jump diffusion and the time change as an absolutely continuous stochastic process with seasonal component. The activity rate of the stochastic time change can be related to the factors that influence supply and demand. Here we use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change, and show that this choice leads to realistic price paths. We derive properties of the resulting price process and develop the model calibration procedure. We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths by Monte Carlo simulations. We show that the simulated price process matches the distributional characteristics of the observed electricity prices in periods of both high and low demand. - Highlights: • We develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. • We incorporate the characteristic features of electricity prices, such as seasonal volatility and spikes into the model. • We use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change • We derive properties of the resulting price process and develop the model calibration procedure. • We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths.

  6. From stochastic completion fields to tensor voting

    NARCIS (Netherlands)

    Almsick, van M.A.; Duits, R.; Franken, E.M.; Haar Romenij, ter B.M.; Olsen, O.F.; Florack, L.M.J.; Kuijper, A.

    2005-01-01

    Several image processing algorithms imitate the lateral interaction of neurons in the visual striate cortex V1 to account for the correlations along contours and lines. Here we focus on two methodologies: tensor voting by Guy and Medioni, and stochastic completion fields by Mumford, Williams and

  7. Stochastic programming with integer recourse

    NARCIS (Netherlands)

    van der Vlerk, Maarten Hendrikus

    1995-01-01

    In this thesis we consider two-stage stochastic linear programming models with integer recourse. Such models are at the intersection of two different branches of mathematical programming. On the one hand some of the model parameters are random, which places the problem in the field of stochastic

  8. Stochastic volatility and stochastic leverage

    DEFF Research Database (Denmark)

    Veraart, Almut; Veraart, Luitgard A. M.

    This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...... models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new...

  9. Stochastic Modelling of Hydrologic Systems

    DEFF Research Database (Denmark)

    Jonsdottir, Harpa

    2007-01-01

    In this PhD project several stochastic modelling methods are studied and applied on various subjects in hydrology. The research was prepared at Informatics and Mathematical Modelling at the Technical University of Denmark. The thesis is divided into two parts. The first part contains...... an introduction and an overview of the papers published. Then an introduction to basic concepts in hydrology along with a description of hydrological data is given. Finally an introduction to stochastic modelling is given. The second part contains the research papers. In the research papers the stochastic methods...... are described, as at the time of publication these methods represent new contribution to hydrology. The second part also contains additional description of software used and a brief introduction to stiff systems. The system in one of the papers is stiff....

  10. A General Stochastic Maximum Principle for SDEs of Mean-field Type

    International Nuclear Information System (INIS)

    Buckdahn, Rainer; Djehiche, Boualem; Li Juan

    2011-01-01

    We study the optimal control for stochastic differential equations (SDEs) of mean-field type, in which the coefficients depend on the state of the solution process as well as of its expected value. Moreover, the cost functional is also of mean-field type. This makes the control problem time inconsistent in the sense that the Bellman optimality principle does not hold. For a general action space a Peng’s-type stochastic maximum principle (Peng, S.: SIAM J. Control Optim. 2(4), 966–979, 1990) is derived, specifying the necessary conditions for optimality. This maximum principle differs from the classical one in the sense that here the first order adjoint equation turns out to be a linear mean-field backward SDE, while the second order adjoint equation remains the same as in Peng’s stochastic maximum principle.

  11. Stochasticity and determinism in models of hematopoiesis.

    Science.gov (United States)

    Kimmel, Marek

    2014-01-01

    This chapter represents a novel view of modeling in hematopoiesis, synthesizing both deterministic and stochastic approaches. Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis. New types of models are on the horizon. These models attempt to account for distributed environments such as hematopoietic niches and their impact on dynamics. Mixed effects of such structures and chance events are largely unknown and constitute both a challenge and promise for modeling. Our discussion is presented under the separate headings of deterministic and stochastic modeling; however, the connections between both are frequently mentioned. Four case studies are included to elucidate important examples. We also include a primer of deterministic and stochastic dynamics for the reader's use.

  12. Dynamic phase transitions and dynamic phase diagrams of the spin-2 Blume-Capel model under an oscillating magnetic field within the effective-field theory

    Energy Technology Data Exchange (ETDEWEB)

    Ertas, Mehmet [Department of Physics, Erciyes University, 38039 Kayseri (Turkey); Institute of Science, Erciyes University, 38039 Kayseri (Turkey); Deviren, Bayram [Department of Physics, Nevsehir University, 50300 Nevsehir (Turkey); Keskin, Mustafa, E-mail: keskin@erciyes.edu.tr [Department of Physics, Erciyes University, 38039 Kayseri (Turkey)

    2012-03-15

    The dynamic phase transitions are studied in the kinetic spin-2 Blume-Capel model under a time-dependent oscillating magnetic field using the effective-field theory with correlations. The effective-field dynamic equation for the average magnetization is derived by employing the Glauber transition rates and the phases in the system are obtained by solving this dynamic equation. The nature (first- or second-order) of the dynamic phase transition is characterized by investigating the thermal behavior of the dynamic magnetization and the dynamic phase transition temperatures are obtained. The dynamic phase diagrams are constructed in the reduced temperature and magnetic field amplitude plane and are of seven fundamental types. Phase diagrams contain the paramagnetic (P), ferromagnetic-2 (F{sub 2}) and three coexistence or mixed phase regions, namely the F{sub 2}+P, F{sub 1}+P and F{sub 2}+F{sub 1}+P, which strongly depend on the crystal-field interaction (D) parameter. The system also exhibits the dynamic tricritical behavior. - Highlights: Black-Right-Pointing-Pointer Dynamic phase transitions are studied in spin-2 BC model using EFT. Black-Right-Pointing-Pointer Dynamic phase diagrams are constructed in (T/zJ, h/zJ) plane. Black-Right-Pointing-Pointer Seven fundamental types of dynamic phase diagrams are found in the system. Black-Right-Pointing-Pointer System exhibits dynamic tricritical behavior.

  13. Optically levitated nanoparticle as a model system for stochastic bistable dynamics.

    Science.gov (United States)

    Ricci, F; Rica, R A; Spasenović, M; Gieseler, J; Rondin, L; Novotny, L; Quidant, R

    2017-05-09

    Nano-mechanical resonators have gained an increasing importance in nanotechnology owing to their contributions to both fundamental and applied science. Yet, their small dimensions and mass raises some challenges as their dynamics gets dominated by nonlinearities that degrade their performance, for instance in sensing applications. Here, we report on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle in high vacuum. We demonstrate how it can lead to efficient signal amplification schemes, including stochastic resonance. This work contributes to showing the use of levitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wide variety of fields.

  14. Expansions of general stationary stochastic optical fields: general formalism

    International Nuclear Information System (INIS)

    Martinez-Herrero, R.; Mejias, P.M.

    1985-01-01

    A new expansion of a general stationary stochastic optical field is derived. Each term of the series is seen to represent a recently defined new class of optical fields, the so-called spectrally quasi-factorizable fields. Alternative expansion in terms of nonstationary fields that obey the wave equation is also shown. A relationship between temporal and spatial features of stationary free optical fields is discussed

  15. Distribution of particles in stochastic electric fields

    International Nuclear Information System (INIS)

    Rolland, Paul.

    1979-11-01

    The distribution of one particle as well as an ensemble of particles submitted to a stochastic electric field obeying different kinds of laws is studied. A particular attention is devoted to the deviation from the gaussian distribution and to the consequences of this effect on diffusion and heating [fr

  16. Stochastic Models of Polymer Systems

    Science.gov (United States)

    2016-01-01

    Distribution Unlimited Final Report: Stochastic Models of Polymer Systems The views, opinions and/or findings contained in this report are those of the...ADDRESS. Princeton University PO Box 0036 87 Prospect Avenue - 2nd floor Princeton, NJ 08544 -2020 14-Mar-2014 ABSTRACT Number of Papers published in...peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Stochastic Models of Polymer Systems Report Title

  17. Comments on the use of stochastic processes in the field of the ionizing radiations

    International Nuclear Information System (INIS)

    Alvarez Romero, Jose T.

    2008-01-01

    . Although models exist for the former one with stochastic processes such as birth-death, Poisson, two state or mixed states methods (Tan 1991), the conventional literature in radiobiology does not go beyond traditional target models of the Poisson or exponential type. Such approaches are dealt with by Rossi, Zaider, Goodhead, etc. in microdosimetry and radiobiology. Finally, in radiation protection the dose-effect projection models which back-up the recommendations of ICRP or from BEIR, probability functions, projection of regression time dependent models appear, buy very little, if any, is mentioned about their properties from the point of view of stochastic processes. Summing up, very little has been said about the nature of the stochastic background describing physical, chemical, biological and risk phenomena which are present in the field of ionizing radiations. In short, it would be nice to learn if there are processes such as: Gauss, Markov, branching, birth-death, Weiner, Poisson, stationary state methods or other ones to deal with these questions and further, to clarify the mathematical conditions they satisfy together with their significance and phenomenological consequences. (author)

  18. CAM Stochastic Volatility Model for Option Pricing

    Directory of Open Access Journals (Sweden)

    Wanwan Huang

    2016-01-01

    Full Text Available The coupled additive and multiplicative (CAM noises model is a stochastic volatility model for derivative pricing. Unlike the other stochastic volatility models in the literature, the CAM model uses two Brownian motions, one multiplicative and one additive, to model the volatility process. We provide empirical evidence that suggests a nontrivial relationship between the kurtosis and skewness of asset prices and that the CAM model is able to capture this relationship, whereas the traditional stochastic volatility models cannot. We introduce a control variate method and Monte Carlo estimators for some of the sensitivities (Greeks of the model. We also derive an approximation for the characteristic function of the model.

  19. Phase transition and gravitational wave phenomenology of scalar conformal extensions of the Standard Model

    Energy Technology Data Exchange (ETDEWEB)

    Marzola, Luca; Racioppi, Antonio; Vaskonen, Ville [National Institute of Chemical Physics and Biophysics, Tallinn (Estonia)

    2017-07-15

    Thermal corrections in classically conformal models typically induce a strong first-order electroweak phase transition, thereby resulting in a stochastic gravitational background that could be detectable at gravitational wave observatories. After reviewing the basics of classically conformal scenarios, in this paper we investigate the phase transition dynamics in a thermal environment and the related gravitational wave phenomenology within the framework of scalar conformal extensions of the Standard Model. We find that minimal extensions involving only one additional scalar field struggle to reproduce the correct phase transition dynamics once thermal corrections are accounted for. Next-to-minimal models, instead, yield the desired electroweak symmetry breaking and typically result in a very strong gravitational wave signal. (orig.)

  20. Stochastic quantization and mean field approximation

    International Nuclear Information System (INIS)

    Jengo, R.; Parga, N.

    1983-09-01

    In the context of the stochastic quantization we propose factorized approximate solutions for the Fokker-Planck equation for the XY and Zsub(N) spin systems in D dimensions. The resulting differential equation for a factor can be solved and it is found to give in the limit of t→infinity the mean field or, in the more general case, the Bethe-Peierls approximation. (author)

  1. Stochastic Finite Elements in Reliability-Based Structural Optimization

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Engelund, S.

    1995-01-01

    Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect to optimi......Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect...... to optimization variables can be performed. A computer implementation is described and an illustrative example is given....

  2. An effective streamflow process model for optimal reservoir operation using stochastic dual dynamic programming

    OpenAIRE

    Raso , L.; Malaterre , P.O.; Bader , J.C.

    2017-01-01

    International audience; This article presents an innovative streamflow process model for use in reservoir operational rule design in stochastic dual dynamic programming (SDDP). Model features, which can be applied independently, are (1) a multiplicative process model for the forward phase and its linearized version for the backward phase; and (2) a nonuniform time-step length that is inversely proportional to seasonal variability. The advantages are (1) guaranteeing positive streamflow values...

  3. Phase-field models of microstructure evolution in a system with elastic inhomogeneity and defects

    Science.gov (United States)

    Hu, Shenyang

    In this thesis, the phase-field approach is employed to study the effect of elastic inhomogeneity and structural defects on phase separation kinetics and morphological evolution in bulk and film systems, the precipitation of theta ' phase (Al2Cu) in Al-Cu alloys, and solute strengthening of alloys. By combining the iteration method for calculating the elastic energy and a semi-implicit spectral method for solving the Cahn-Hilliard equation an extremely efficient phase-field model is developed for studying morphological evolution in coherent systems with large elastic inhomogeneity. Spinodal decomposition in a thin film with periodically distributed arrays of interfacial dislocations is simulated. The results show that the periodic stress field associated with the array of interfacial dislocations leads to a directional phase separation and the formation of ordered microstructures. The metastable theta' (Al2Cu) precipitates are one of the primary strengthening precipitates in Al-Cu alloys. They are of a plate-like shape with strong interfacial energy and mobility anisotropies. A phase-field model which can automatically incorporate the thermodynamic and kinetic information from databases is developed. The relationships between phase-field model parameters and material thermodynamic and kinetic properties are established. Systematic simulations of theta' growth in 1D, 2D and 3D are carried out. The growth of a single theta ' precipitate in 1D exactly reproduces the results from analytical solutions. The phase-filed model can serve as a basis for quantitative understanding of the influence of elastic energy, interface energy anisotropy and interface mobility anisotropy on the precipitation of theta' in Al-Cu alloys. Precipitates and solutes are commonly used to strengthen alloys. A phase field model of dislocation dynamics, which employs 12 order parameter fields to describe the dislocation distribution in a single fcc crystal, and one composition field to describe

  4. Modeling stochasticity and robustness in gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

    2009-06-15

    Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

  5. Stochastic modeling of reinforced concrete structures exposed to chloride attack

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Frier, Christian

    2004-01-01

    For many reinforced concrete structures corrosion of reinforcement is an important problem since it can result in expensive maintenance and repair actions. Further, a significant reduction of the load-bearing capacity can occur. One mode of corrosion initiation is that the chloride content around...... concentration and reinforcement cover depth are modeled by stochastic fields. The paper contains a description of the parameters to be included in a stochastic model and a proposal for the information needed to obtain values for the parameters in order to be able to perform reliability investigations....... The distribution of the time to initiation of corrosion is estimated by simulation. As an example a bridge pier in a marine environment is considered....

  6. Stochastic Modeling of Reinforced Concrete Structures Exposed to Chloride Attack

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Frier, Christian

    2003-01-01

    For many reinforced concrete structures corrosion of reinforcement is an important problem since it can result in expensive maintenance and repair actions. Further, a significant reduction of the load-bearing capacity can occur. One mode of corrosion initiation is that the chloride content around...... concentration and reinforcement cover depth are modeled by stochastic fields. The paper contains a description of the parameters to be included in a stochastic model and a proposal for the information needed to obtain values for the parameters in order to be ab le to perform reliability investigations....... The distribution of the time to initiation of corrosion is estimated by simulation. As an example a bridge pier in a marine environment is considered....

  7. The role of stochasticity in sawtooth oscillation

    International Nuclear Information System (INIS)

    Lichtenberg, A.J.; Itoh, Kimitaka; Itoh, Sanae; Fukuyama, Atsushi.

    1991-08-01

    In this paper we have demonstrated that stochastization of field lines, resulting from the interaction of the fundamental m/n=1/1 helical mode with other periodicities, plays an important role in sawtooth oscillations. The time scale for the stochastic temperature diffusion has been determined. It was shown to be sufficiently fast to account for the fast sawtooth crash, and is generally shorter than the time scales for the redistribution of current. The enhancement of the electron and ion viscosity, arising from the stochastic field lines, has been calculated. The enhanced electron viscosity always leads to an initial increase in the growth rate of the mode; the enhanced ion viscosity can ultimately lead to mode stabilization before a complete temperature redistribution or flux reconnection has occurred. A dynamical model has been introduced to calculate the path of the sawtooth oscillation through a parameter space of shear and amplitude of the helical perturbation. The stochastic trigger to the enhanced growth rate and the stabilization by the ion viscosity are also included in the mode. A reasonable prescription for the flux reconnection at the end of the growth phase allows us to determine the initial q-value for the successive sawtooth ramps. (J.P.N.)

  8. Stochastic dynamics of new inflation

    International Nuclear Information System (INIS)

    Nakao, Ken-ichi; Nambu, Yasusada; Sasaki, Misao.

    1988-07-01

    We investigate thoroughly the dynamics of an inflation-driving scalar field in terms of an extended version of the stochastic approach proposed by Starobinsky and discuss the spacetime structure of the inflationary universe. To avoid any complications which might arise due to quantum gravity, we concentrate our discussions on the new inflationary universe scenario in which all the energy scales involved are well below the planck mass. The investigation is done both analytically and numerically. In particular, we present a full numerical analysis of the stochastic scalar field dynamics on the phase space. Then implications of the results are discussed. (author)

  9. Stochastic sensitivity analysis of the variability of dynamics and transition to chaos in the business cycles model

    Science.gov (United States)

    Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana

    2018-01-01

    A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.

  10. Intimate Partner Violence: A Stochastic Model.

    Science.gov (United States)

    Guidi, Elisa; Meringolo, Patrizia; Guazzini, Andrea; Bagnoli, Franco

    2017-01-01

    Intimate partner violence (IPV) has been a well-studied problem in the past psychological literature, especially through its classical methodology such as qualitative, quantitative and mixed methods. This article introduces two basic stochastic models as an alternative approach to simulate the short and long-term dynamics of a couple at risk of IPV. In both models, the members of the couple may assume a finite number of states, updating them in a probabilistic way at discrete time steps. After defining the transition probabilities, we first analyze the evolution of the couple in isolation and then we consider the case in which the individuals modify their behavior depending on the perceived violence from other couples in their environment or based on the perceived informal social support. While high perceived violence in other couples may converge toward the own presence of IPV by means a gender-specific transmission, the gender differences fade-out in the case of received informal social support. Despite the simplicity of the two stochastic models, they generate results which compare well with past experimental studies about IPV and they give important practical implications for prevention intervention in this field. Copyright: © 2016 by Fabrizio Serra editore, Pisa · Roma.

  11. Dynamical phase transitions in spin models and automata

    International Nuclear Information System (INIS)

    Derrida, B.

    1989-01-01

    Some of the models and methods developed in the study of the dynamics of spin models and automata are described. Special attention is given to the distance method which consists of comparing the time evolution of two configurations. The method is used to obtain the phase boundary between a frozen and a chaotic phase in the case of deterministic models. For stochastic systems the method is used to obtain dynamical phase transitions

  12. Development of stochastic indicator models of lithology, Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Rautman, C.A.; Robey, T.H.

    1994-01-01

    Indicator geostatistical techniques have been used to produce a number of fully three-dimensional stochastic simulations of large-scale lithologic categories at the Yucca Mountain site. Each realization reproduces the available drill hole data used to condition the simulation. Information is propagated away from each point of observation in accordance with a mathematical model of spatial continuity inferred through soft data taken from published geologic cross sections. Variations among the simulated models collectively represent uncertainty in the lithology at unsampled locations. These stochastic models succeed in capturing many major features of welded-nonwelded lithologic framework of Yucca Mountain. However, contacts between welded and nonwelded rock types for individual simulations appear more complex than suggested by field observation, and a number of probable numerical artifacts exist in these models. Many of the apparent discrepancies between the simulated models and the general geology of Yucca Mountain represent characterization uncertainty, and can be traced to the sparse site data used to condition the simulations. Several vertical stratigraphic columns have been extracted from the three-dimensional stochastic models for use in simplified total-system performance assessment exercises. Simple, manual adjustments are required to eliminate the more obvious simulation artifacts and to impose a secondary set of deterministic geologic features on the overall stratigraphic framework provided by the indictor models

  13. Combining phase-field crystal methods with a Cahn-Hilliard model for binary alloys

    Science.gov (United States)

    Balakrishna, Ananya Renuka; Carter, W. Craig

    2018-04-01

    Diffusion-induced phase transitions typically change the lattice symmetry of the host material. In battery electrodes, for example, Li ions (diffusing species) are inserted between layers in a crystalline electrode material (host). This diffusion induces lattice distortions and defect formations in the electrode. The structural changes to the lattice symmetry affect the host material's properties. Here, we propose a 2D theoretical framework that couples a Cahn-Hilliard (CH) model, which describes the composition field of a diffusing species, with a phase-field crystal (PFC) model, which describes the host-material lattice symmetry. We couple the two continuum models via coordinate transformation coefficients. We introduce the transformation coefficients in the PFC method to describe affine lattice deformations. These transformation coefficients are modeled as functions of the composition field. Using this coupled approach, we explore the effects of coarse-grained lattice symmetry and distortions on a diffusion-induced phase transition process. In this paper, we demonstrate the working of the CH-PFC model through three representative examples: First, we describe base cases with hexagonal and square symmetries for two composition fields. Next, we illustrate how the CH-PFC method interpolates lattice symmetry across a diffuse phase boundary. Finally, we compute a Cahn-Hilliard type of diffusion and model the accompanying changes to lattice symmetry during a phase transition process.

  14. The Covariance and Bicovariance of the Stochastic Neutron Field

    International Nuclear Information System (INIS)

    Perez, R.B.; Mattingly, J.K.; Valentine, T.E.; Mihalczo, J.T.

    2000-01-01

    On the basis of the general stochastic neutron field theory developed by Munoz-Cobo et al, results on the covariance and bicovariance of the neutron field have been presented. These two statistical quantities are obtained from the counts observed in detectors operating during a period of time (gate length), Δ qc . A classical example is the so called Feynmann Y-function that is defined as the variance to mean ratio of the neutron field. Upon taking the limit of the covariance and bicovariance function for Δ qc r a rrow O , one obtains the two and three detector cross correlation functions respectively. The mathematical structure of the results so obtained have a transparent physical interpretation in terms of the space and delay time overlap between the field-of-view of the detectors. For the first time, an expression has been obtained for the bispectrum function of the stochastic neutron field and for the appropriate weight functions to be used as space-energy-angle correction factors for the one-point kinetics approximation

  15. Stochastic formulation of quantum field at finite temperature

    International Nuclear Information System (INIS)

    Lim, S.C.

    1989-01-01

    This paper reports that, based on an extension of the stochastic quantization method of Nelson, it is possible to obtain finite temperature fields in both the imaginary and real time formalisms which are usually quantized by using the functional integral technique

  16. Energy law preserving C0 finite element schemes for phase field models in two-phase flow computations

    International Nuclear Information System (INIS)

    Hua Jinsong; Lin Ping; Liu Chun; Wang Qi

    2011-01-01

    Highlights: → We study phase-field models for multi-phase flow computation. → We develop an energy-law preserving C0 FEM. → We show that the energy-law preserving method work better. → We overcome unphysical oscillation associated with the Cahn-Hilliard model. - Abstract: We use the idea in to develop the energy law preserving method and compute the diffusive interface (phase-field) models of Allen-Cahn and Cahn-Hilliard type, respectively, governing the motion of two-phase incompressible flows. We discretize these two models using a C 0 finite element in space and a modified midpoint scheme in time. To increase the stability in the pressure variable we treat the divergence free condition by a penalty formulation, under which the discrete energy law can still be derived for these diffusive interface models. Through an example we demonstrate that the energy law preserving method is beneficial for computing these multi-phase flow models. We also demonstrate that when applying the energy law preserving method to the model of Cahn-Hilliard type, un-physical interfacial oscillations may occur. We examine the source of such oscillations and a remedy is presented to eliminate the oscillations. A few two-phase incompressible flow examples are computed to show the good performance of our method.

  17. Phase-field modelling and synchrotron validation of phase transformations in martensitic dual-phase steel

    International Nuclear Information System (INIS)

    Thiessen, R.G.; Sietsma, J.; Palmer, T.A.; Elmer, J.W.; Richardson, I.M.

    2007-01-01

    A thermodynamically based method to describe the phase transformations during heating and cooling of martensitic dual-phase steel has been developed, and in situ synchrotron measurements of phase transformations have been undertaken to support the model experimentally. Nucleation routines are governed by a novel implementation of the classical nucleation theory in a general phase-field code. Physically-based expressions for the temperature-dependent interface mobility and the driving forces for transformation have also been constructed. Modelling of martensite was accomplished by assuming a carbon supersaturation of the body-centred-cubic ferrite lattice. The simulations predict kinetic aspects of the austenite formation during heating and ferrite formation upon cooling. Simulations of partial austenitising thermal cycles predicted peak and retained austenite percentages of 38.2% and 6.7%, respectively, while measurements yielded peak and retained austenite percentages of 31.0% and 7.2% (±1%). Simulations of a complete austenitisation thermal cycle predicted the measured complete austenitisation and, upon cooling, a retained austenite percentage of 10.3% while 9.8% (±1%) retained austenite was measured

  18. Stochastic models in reliability and maintenance

    CERN Document Server

    2002-01-01

    Our daily lives can be maintained by the high-technology systems. Computer systems are typical examples of such systems. We can enjoy our modern lives by using many computer systems. Much more importantly, we have to maintain such systems without failure, but cannot predict when such systems will fail and how to fix such systems without delay. A stochastic process is a set of outcomes of a random experiment indexed by time, and is one of the key tools needed to analyze the future behavior quantitatively. Reliability and maintainability technologies are of great interest and importance to the maintenance of such systems. Many mathematical models have been and will be proposed to describe reliability and maintainability systems by using the stochastic processes. The theme of this book is "Stochastic Models in Reliability and Main­ tainability. " This book consists of 12 chapters on the theme above from the different viewpoints of stochastic modeling. Chapter 1 is devoted to "Renewal Processes," under which cla...

  19. The multivariate supOU stochastic volatility model

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Stelzer, Robert

    Using positive semidefinite supOU (superposition of Ornstein-Uhlenbeck type) processes to describe the volatility, we introduce a multivariate stochastic volatility model for financial data which is capable of modelling long range dependence effects. The finiteness of moments and the second order...... structure of the volatility, the log returns, as well as their "squares" are discussed in detail. Moreover, we give several examples in which long memory effects occur and study how the model as well as the simple Ornstein-Uhlenbeck type stochastic volatility model behave under linear transformations....... In particular, the models are shown to be preserved under invertible linear transformations. Finally, we discuss how (sup)OU stochastic volatility models can be combined with a factor modelling approach....

  20. A Fractionally Integrated Wishart Stochastic Volatility Model

    NARCIS (Netherlands)

    M. Asai (Manabu); M.J. McAleer (Michael)

    2013-01-01

    textabstractThere has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of

  1. Phase-field model of insulator-to-metal transition in VO2 under an electric field

    Science.gov (United States)

    Shi, Yin; Chen, Long-Qing

    2018-05-01

    The roles of an electric field and electronic doping in insulator-to-metal transitions are still not well understood. Here we formulated a phase-field model of insulator-to-metal transitions by taking into account both structural and electronic instabilities as well as free electrons and holes in VO2, a strongly correlated transition-metal oxide. Our phase-field simulations demonstrate that in a VO2 slab under a uniform electric field, an abrupt universal resistive transition occurs inside the supercooling region, in sharp contrast to the conventional Landau-Zener smooth electric breakdown. We also show that hole doping may decouple the structural and electronic phase transitions in VO2, leading to a metastable metallic monoclinic phase which could be stabilized through a geometrical confinement and the size effect. This work provides a general mesoscale thermodynamic framework for understanding the influences of electric field, electronic doping, and stress and strain on insulator-to-metal transitions and the corresponding mesoscale domain structure evolution in VO2 and related strongly correlated systems.

  2. 2D phase field modeling of sintering of silver nanoparticles

    NARCIS (Netherlands)

    Chockalingam, K.; Kouznetsova, V.; van der Sluis, O.; Geers, M.G.D.

    2016-01-01

    The sintering mechanism of silver nanoparticles is modelled by incorporating surface, volume and grain boundary diffusion in a phase field model. A direction-dependent tensorial mobility formulation is adopted, capturing the fact that diffusion mainly occurs along the directions tangential to the

  3. Stochastic optimized life cycle models for risk mitigation in power system applications

    International Nuclear Information System (INIS)

    Sageder, A.

    1998-01-01

    This ork shows the relevance of stochastic optimization in complex power system applications. It was proven that usual deterministic mean value models not only predict inaccurate results but are also most often on the risky side. The change in the market effects all kind of evaluation processes (e.g. fuel type and technology but especially financial engineering evaluations) in the endeavor of a strict risk mitigation comparison. But not only IPPs also traditional Utilities dash for risk/return optimized investment opportunities. In this study I developed a 2-phase model which can support a decision-maker in finding optimal solutions on investment and profitability. It has to be stated, that in this study no objective function will be optimized in an algorithmically way. On the one hand focus is laid on finding optimal solutions out of different choices (highest return at lowest possible risk); on the other hand the endeavor was to provide a decision makers with a better assessment of the likelihood of outcomes on investment considerations. The first (deterministic) phase computes in a Total Cost of Ownership (TCO) approach (Life cycle Calculation; DCF method). Most of the causal relations (day of operation, escalation of personal expanses, inflation, depreciation period, etc.) are defined within this phase. The second (stochastic) phase is a total new way in optimizing risk/return relations. With the some decision theory mathematics an expected value of stochastic solutions can be calculated. Furthermore probability function have to be defined out of historical data. The model not only supports profitability analysis (including regress and sensitivity analysis) but also supports a decision-maker in a decision process. Emphasis was laid on risk-return analysis, which can give the decision-maker first hand informations of the type of risk return problem (risk concave, averse or linear). Five important parameters were chosen which have the characteristics of typical

  4. A stochastic SIS epidemic model with vaccination

    Science.gov (United States)

    Cao, Boqiang; Shan, Meijing; Zhang, Qimin; Wang, Weiming

    2017-11-01

    In this paper, we investigate the basic features of an SIS type infectious disease model with varying population size and vaccinations in presence of environment noise. By applying the Markov semigroup theory, we propose a stochastic reproduction number R0s which can be seen as a threshold parameter to utilize in identifying the stochastic extinction and persistence: If R0s disease-free absorbing set for the stochastic epidemic model, which implies that disease dies out with probability one; while if R0s > 1, under some mild extra conditions, the SDE model has an endemic stationary distribution which results in the stochastic persistence of the infectious disease. The most interesting finding is that large environmental noise can suppress the outbreak of the disease.

  5. Creating physically-based three-dimensional microstructures: Bridging phase-field and crystal plasticity models.

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Hojun [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Owen, Steven J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Abdeljawad, Fadi F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hanks, Byron [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Battaile, Corbett Chandler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-09-01

    In order to better incorporate microstructures in continuum scale models, we use a novel finite element (FE) meshing technique to generate three-dimensional polycrystalline aggregates from a phase field grain growth model of grain microstructures. The proposed meshing technique creates hexahedral FE meshes that capture smooth interfaces between adjacent grains. Three dimensional realizations of grain microstructures from the phase field model are used in crystal plasticity-finite element (CP-FE) simulations of polycrystalline a -iron. We show that the interface conformal meshes significantly reduce artificial stress localizations in voxelated meshes that exhibit the so-called "wedding cake" interfaces. This framework provides a direct link between two mesoscale models - phase field and crystal plasticity - and for the first time allows mechanics simulations of polycrystalline materials using three-dimensional hexahedral finite element meshes with realistic topological features.

  6. Matrix model approximations of fuzzy scalar field theories and their phase diagrams

    Energy Technology Data Exchange (ETDEWEB)

    Tekel, Juraj [Department of Theoretical Physics, Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynska Dolina, Bratislava, 842 48 (Slovakia)

    2015-12-29

    We present an analysis of two different approximations to the scalar field theory on the fuzzy sphere, a nonperturbative and a perturbative one, which are both multitrace matrix models. We show that the former reproduces a phase diagram with correct features in a qualitative agreement with the previous numerical studies and that the latter gives a phase diagram with features not expected in the phase diagram of the field theory.

  7. The quantum-field renormalization group in the problem of a growing phase boundary

    International Nuclear Information System (INIS)

    Antonov, N.V.; Vasil'ev, A.N.

    1995-01-01

    Within the quantum-field renormalization-group approach we examine the stochastic equation discussed by S.I. Pavlik in describing a randomly growing phase boundary. We show that, in contrast to Pavlik's assertion, the model is not multiplicatively renormalizable and that its consistent renormalization-group analysis requires introducing an infinite number of counterterms and the respective coupling constants (open-quotes chargeclose quotes). An explicit calculation in the one-loop approximation shows that a two-dimensional surface of renormalization-group points exits in the infinite-dimensional charge space. If the surface contains an infrared stability region, the problem allows for scaling with the nonuniversal critical dimensionalities of the height of the phase boundary and time, δ h and δ t , which satisfy the exact relationship 2 δ h = δ t + d, where d is the dimensionality of the phase boundary. 23 refs., 1 tab

  8. Kinetics of the spin-2 Blume-Capel model under a time-dependent oscillating external field

    International Nuclear Information System (INIS)

    Keskin, M.; Canko, O.; Ertas, M.

    2007-01-01

    Within a mean-field approach and using the Glauber-type stochastic dynamics, we study the kinetics of the spin-2 Blume-Capel model in the presence of a time-varying (sinusoidal) magnetic field. We investigate the time dependence of the average order parameter and the behavior of the average order parameter in a period, which is also called the dynamic order parameter, as a function of the reduced temperature. The nature (continuous and discontinuous) of the transition is characterized by the dynamic order parameter. The dynamic phase transition points are obtained and the phase diagrams are presented in the reduced magnetic field amplitude and reduced temperature plane. The phase diagrams exhibit one dynamic tricritical point; besides a disordered and an ordered phases, there are three phase coexistence regions that are strongly dependent on the interaction parameter

  9. Magnetic islands modelled by a phase-field-crystal approach

    Science.gov (United States)

    Faghihi, Niloufar; Mkhonta, Simiso; Elder, Ken R.; Grant, Martin

    2018-03-01

    Using a minimal model based on the phase-field-crystal formalism, we study the coupling between the density and magnetization in ferromagnetic solids. Analytical calculations for the square phase in two dimensions are presented and the small deformation properties of the system are examined. Furthermore, numerical simulations are conducted to study the influence of an external magnetic field on various phase transitions, the anisotropic properties of the free energy functional, and the scaling behaviour of the growth of the magnetic domains in a crystalline solid. It is shown that the energy of the system can depend on the direction of the magnetic moments, with respect to the crystalline direction. Furthermore, the growth of the magnetic domains in a crystalline solid is studied and is shown that the growth of domains is in agreement with expected behaviour.

  10. Front propagation and effect of memory in stochastic desertification models with an absorbing state

    Science.gov (United States)

    Herman, Dor; Shnerb, Nadav M.

    2017-08-01

    Desertification in dryland ecosystems is considered to be a major environmental threat that may lead to devastating consequences. The concern increases when the system admits two alternative steady states and the transition is abrupt and irreversible (catastrophic shift). However, recent studies show that the inherent stochasticity of the birth-death process, when superimposed on the presence of an absorbing state, may lead to a continuous (second order) transition even if the deterministic dynamics supports a catastrophic transition. Following these works we present here a numerical study of a one-dimensional stochastic desertification model, where the deterministic predictions are confronted with the observed dynamics. Our results suggest that a stochastic spatial system allows for a propagating front only when its active phase invades the inactive (desert) one. In the extinction phase one observes transient front propagation followed by a global collapse. In the presence of a seed bank the vegetation state is shown to be more robust against demographic stochasticity, but the transition in that case still belongs to the directed percolation equivalence class.

  11. Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

    Science.gov (United States)

    Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

    2015-05-15

    The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Consistent Stochastic Modelling of Meteocean Design Parameters

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Sterndorff, M. J.

    2000-01-01

    Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...

  13. 3D stochastic inversion and joint inversion of potential fields for multi scale parameters

    Science.gov (United States)

    Shamsipour, Pejman

    In this thesis we present the development of new techniques for the interpretation of potential field (gravity and magnetic data), which are the most widespread economic geophysical methods used for oil and mineral exploration. These new techniques help to address the long-standing issue with the interpretation of potential fields, namely the intrinsic non-uniqueness inversion of these types of data. The thesis takes the form of three papers (four including Appendix), which have been published, or soon to be published, in respected international journals. The purpose of the thesis is to introduce new methods based on 3D stochastical approaches for: 1) Inversion of potential field data (magnetic), 2) Multiscale Inversion using surface and borehole data and 3) Joint inversion of geophysical potential field data. We first present a stochastic inversion method based on a geostatistical approach to recover 3D susceptibility models from magnetic data. The aim of applying geostatistics is to provide quantitative descriptions of natural variables distributed in space or in time and space. We evaluate the uncertainty on the parameter model by using geostatistical unconditional simulations. The realizations are post-conditioned by cokriging to observation data. In order to avoid the natural tendency of the estimated structure to lay near the surface, depth weighting is included in the cokriging system. Then, we introduce algorithm for multiscale inversion, the presented algorithm has the capability of inverting data on multiple supports. The method involves four main steps: i. upscaling of borehole parameters (It could be density or susceptibility) to block parameters, ii. selection of block to use as constraints based on a threshold on kriging variance, iii. inversion of observation data with selected block densities as constraints, and iv. downscaling of inverted parameters to small prisms. Two modes of application are presented: estimation and simulation. Finally, a novel

  14. Stochastic hyperfine interactions modeling library

    Science.gov (United States)

    Zacate, Matthew O.; Evenson, William E.

    2011-04-01

    The stochastic hyperfine interactions modeling library (SHIML) provides a set of routines to assist in the development and application of stochastic models of hyperfine interactions. The library provides routines written in the C programming language that (1) read a text description of a model for fluctuating hyperfine fields, (2) set up the Blume matrix, upon which the evolution operator of the system depends, and (3) find the eigenvalues and eigenvectors of the Blume matrix so that theoretical spectra of experimental techniques that measure hyperfine interactions can be calculated. The optimized vector and matrix operations of the BLAS and LAPACK libraries are utilized; however, there was a need to develop supplementary code to find an orthonormal set of (left and right) eigenvectors of complex, non-Hermitian matrices. In addition, example code is provided to illustrate the use of SHIML to generate perturbed angular correlation spectra for the special case of polycrystalline samples when anisotropy terms of higher order than A can be neglected. Program summaryProgram title: SHIML Catalogue identifier: AEIF_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIF_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU GPL 3 No. of lines in distributed program, including test data, etc.: 8224 No. of bytes in distributed program, including test data, etc.: 312 348 Distribution format: tar.gz Programming language: C Computer: Any Operating system: LINUX, OS X RAM: Varies Classification: 7.4 External routines: TAPP [1], BLAS [2], a C-interface to BLAS [3], and LAPACK [4] Nature of problem: In condensed matter systems, hyperfine methods such as nuclear magnetic resonance (NMR), Mössbauer effect (ME), muon spin rotation (μSR), and perturbed angular correlation spectroscopy (PAC) measure electronic and magnetic structure within Angstroms of nuclear probes through the hyperfine interaction. When

  15. Stochastic model of the spinning electron

    International Nuclear Information System (INIS)

    Simaciu, I.; Borsos, Z.

    2002-01-01

    In Stochastic Electrodynamics (SED) it is demonstrated that electrostatic interaction is the result of the scattering of the Classical Zero-Point Field (CZPF) background by the charged particles. In such models, the electron is modelled as a two-dimensional oscillator, which interacts with the electric component of the CZPF background. The electron with spin is not only an electric monopole but also a magnetic dipole. The interaction of the spin electron with the CZPF background is not only electric but also magnetic. We calculate the scattering cross-section of magnetic dipole in the situation when a magnetic field, variable in time B arrow = B 0 arrow sin ωt, acts over the rigid magnetic dipole given by the symmetry of the model. The cross-section of a magnetic dipole σ m must be equal to the cross-section of an electric monopole σ e . This equality between σ m and σ e cross-sections is motivated, too, by the fact that, in the model of the two-dimensional oscillator, the electric charge q e has the motion speed c. (authors)

  16. A simplified BBGKY hierarchy for correlated fermions from a stochastic mean-field approach

    International Nuclear Information System (INIS)

    Lacroix, Denis; Tanimura, Yusuke; Ayik, Sakir; Yilmaz, Bulent

    2016-01-01

    The stochastic mean-field (SMF) approach allows to treat correlations beyond mean-field using a set of independent mean-field trajectories with appropriate choice of fluctuating initial conditions. We show here that this approach is equivalent to a simplified version of the Bogolyubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy between one-, two-,.., N -body degrees of freedom. In this simplified version, one-body degrees of freedom are coupled to fluctuations to all orders while retaining only specific terms of the general BBGKY hierarchy. The use of the simplified BBGKY is illustrated with the Lipkin-Meshkov-Glick (LMG) model. We show that a truncated version of this hierarchy can be useful, as an alternative to the SMF, especially in the weak coupling regime to get physical insight in the effect beyond mean-field. In particular, it leads to approximate analytical expressions for the quantum fluctuations both in the weak and strong coupling regime. In the strong coupling regime, it can only be used for short time evolution. In that case, it gives information on the evolution time-scale close to a saddle point associated to a quantum phase-transition. For long time evolution and strong coupling, we observed that the simplified BBGKY hierarchy cannot be truncated and only the full SMF with initial sampling leads to reasonable results. (orig.)

  17. Stochastic quantization of the Kink solution of phi4 field theory

    International Nuclear Information System (INIS)

    Kates, R.; Rosenblum, A.

    1989-01-01

    The method of Parisi-Wu Stochastic quantization in quantum field theory is compared to earlier work in classical field equations. The method is applied to solve for the propagator for Phi 4 field theory by perturbing the Kink solution

  18. Phase field model for the study of boiling; Modele de champ de phase pour l'etude de l'ebullition

    Energy Technology Data Exchange (ETDEWEB)

    Ruyer, P

    2006-07-15

    This study concerns both the modeling and the numerical simulation of boiling flows. First we propose a review concerning nucleate boiling at high wall heat flux and focus more particularly on the current understanding of the boiling crisis. From this analysis we deduce a motivation for the numerical simulation of bubble growth dynamics. The main and remaining part of this study is then devoted to the development and analyze of a phase field model for the liquid-vapor flows with phase change. We propose a thermodynamic quasi-compressible formulation whose properties match the one required for the numerical study envisaged. The system of governing equations is a thermodynamically consistent regularization of the sharp interface model, that is the advantage of the di use interface models. We show that the thickness of the interface transition layer can be defined independently from the thermodynamic description of the bulk phases, a property that is numerically attractive. We derive the kinetic relation that allows to analyze the consequences of the phase field formulation on the model of the dissipative mechanisms. Finally we study the numerical resolution of the model with the help of simulations of phase transition in simple configurations as well as of isothermal bubble dynamics. (author)

  19. Higgs and confinement phases in the fundamental SU(2) Higgs model: Mean field analysis

    International Nuclear Information System (INIS)

    Damgaard, P.H.; Heller, U.M.

    1985-01-01

    The phase diagram of the four-dimensional SU(2) gauge-Higgs model with Higgs field in the fundamental representation is derived by mean field techniques. When the Higgs field is allowed to fluctuate in. Magnitude, the analytic connection between Higgs and confinement phases breaks down for sufficiently small values of the quark Higgs coupling, indicating that the Higgs and confinement phases for these couplings are strictly distinct phases. (orig.)

  20. Statistical inference and comparison of stochastic models for the hydraulic conductivity at the Finnsjoen-site

    International Nuclear Information System (INIS)

    Norman, S.

    1992-04-01

    The origin of this study was to find a good, or even the best, stochastic model for the hydraulic conductivity field at the Finnsjoe site. The conductivity field in question are regularized, that is upscaled. The reason for performing regularization of measurement data is primarily the need for long correlation scales. This is needed in order to model reasonably large domains that can be used when describing regional groundwater flow accurately. A theory of regularization is discussed in this report. In order to find the best model, jacknifing is employed to compare different stochastic models. The theory for this method is described. In the act of doing so we also take a look at linear predictor theory, so called kriging, and include a general discussion of stochastic functions and intrinsic random functions. The statistical inference methods for finding the models are also described, in particular regression, iterative generalized regression (IGLSE) and non-parametric variogram estimators. A large amount of results is presented for a regularization scale of 36 metre. (30 refs.) (au)

  1. Stochastic stability and bifurcation in a macroeconomic model

    International Nuclear Information System (INIS)

    Li Wei; Xu Wei; Zhao Junfeng; Jin Yanfei

    2007-01-01

    On the basis of the work of Goodwin and Puu, a new business cycle model subject to a stochastically parametric excitation is derived in this paper. At first, we reduce the model to a one-dimensional diffusion process by applying the stochastic averaging method of quasi-nonintegrable Hamiltonian system. Secondly, we utilize the methods of Lyapunov exponent and boundary classification associated with diffusion process respectively to analyze the stochastic stability of the trivial solution of system. The numerical results obtained illustrate that the trivial solution of system must be globally stable if it is locally stable in the state space. Thirdly, we explore the stochastic Hopf bifurcation of the business cycle model according to the qualitative changes in stationary probability density of system response. It is concluded that the stochastic Hopf bifurcation occurs at two critical parametric values. Finally, some explanations are given in a simply way on the potential applications of stochastic stability and bifurcation analysis

  2. Reconstructing the hidden states in time course data of stochastic models.

    Science.gov (United States)

    Zimmer, Christoph

    2015-11-01

    Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  3. Stochastic volatility models and Kelvin waves

    Science.gov (United States)

    Lipton, Alex; Sepp, Artur

    2008-08-01

    We use stochastic volatility models to describe the evolution of an asset price, its instantaneous volatility and its realized volatility. In particular, we concentrate on the Stein and Stein model (SSM) (1991) for the stochastic asset volatility and the Heston model (HM) (1993) for the stochastic asset variance. By construction, the volatility is not sign definite in SSM and is non-negative in HM. It is well known that both models produce closed-form expressions for the prices of vanilla option via the Lewis-Lipton formula. However, the numerical pricing of exotic options by means of the finite difference and Monte Carlo methods is much more complex for HM than for SSM. Until now, this complexity was considered to be an acceptable price to pay for ensuring that the asset volatility is non-negative. We argue that having negative stochastic volatility is a psychological rather than financial or mathematical problem, and advocate using SSM rather than HM in most applications. We extend SSM by adding volatility jumps and obtain a closed-form expression for the density of the asset price and its realized volatility. We also show that the current method of choice for solving pricing problems with stochastic volatility (via the affine ansatz for the Fourier-transformed density function) can be traced back to the Kelvin method designed in the 19th century for studying wave motion problems arising in fluid dynamics.

  4. Stochastic volatility models and Kelvin waves

    International Nuclear Information System (INIS)

    Lipton, Alex; Sepp, Artur

    2008-01-01

    We use stochastic volatility models to describe the evolution of an asset price, its instantaneous volatility and its realized volatility. In particular, we concentrate on the Stein and Stein model (SSM) (1991) for the stochastic asset volatility and the Heston model (HM) (1993) for the stochastic asset variance. By construction, the volatility is not sign definite in SSM and is non-negative in HM. It is well known that both models produce closed-form expressions for the prices of vanilla option via the Lewis-Lipton formula. However, the numerical pricing of exotic options by means of the finite difference and Monte Carlo methods is much more complex for HM than for SSM. Until now, this complexity was considered to be an acceptable price to pay for ensuring that the asset volatility is non-negative. We argue that having negative stochastic volatility is a psychological rather than financial or mathematical problem, and advocate using SSM rather than HM in most applications. We extend SSM by adding volatility jumps and obtain a closed-form expression for the density of the asset price and its realized volatility. We also show that the current method of choice for solving pricing problems with stochastic volatility (via the affine ansatz for the Fourier-transformed density function) can be traced back to the Kelvin method designed in the 19th century for studying wave motion problems arising in fluid dynamics

  5. Stochastic volatility models and Kelvin waves

    Energy Technology Data Exchange (ETDEWEB)

    Lipton, Alex [Merrill Lynch, Mlfc Main, 2 King Edward Street, London EC1A 1HQ (United Kingdom); Sepp, Artur [Merrill Lynch, 4 World Financial Center, New York, NY 10080 (United States)], E-mail: Alex_Lipton@ml.com, E-mail: Artur_Sepp@ml.com

    2008-08-29

    We use stochastic volatility models to describe the evolution of an asset price, its instantaneous volatility and its realized volatility. In particular, we concentrate on the Stein and Stein model (SSM) (1991) for the stochastic asset volatility and the Heston model (HM) (1993) for the stochastic asset variance. By construction, the volatility is not sign definite in SSM and is non-negative in HM. It is well known that both models produce closed-form expressions for the prices of vanilla option via the Lewis-Lipton formula. However, the numerical pricing of exotic options by means of the finite difference and Monte Carlo methods is much more complex for HM than for SSM. Until now, this complexity was considered to be an acceptable price to pay for ensuring that the asset volatility is non-negative. We argue that having negative stochastic volatility is a psychological rather than financial or mathematical problem, and advocate using SSM rather than HM in most applications. We extend SSM by adding volatility jumps and obtain a closed-form expression for the density of the asset price and its realized volatility. We also show that the current method of choice for solving pricing problems with stochastic volatility (via the affine ansatz for the Fourier-transformed density function) can be traced back to the Kelvin method designed in the 19th century for studying wave motion problems arising in fluid dynamics.

  6. Stochastic Modelling Of The Repairable System

    Directory of Open Access Journals (Sweden)

    Andrzejczak Karol

    2015-11-01

    Full Text Available All reliability models consisting of random time factors form stochastic processes. In this paper we recall the definitions of the most common point processes which are used for modelling of repairable systems. Particularly this paper presents stochastic processes as examples of reliability systems for the support of the maintenance related decisions. We consider the simplest one-unit system with a negligible repair or replacement time, i.e., the unit is operating and is repaired or replaced at failure, where the time required for repair and replacement is negligible. When the repair or replacement is completed, the unit becomes as good as new and resumes operation. The stochastic modelling of recoverable systems constitutes an excellent method of supporting maintenance related decision-making processes and enables their more rational use.

  7. Extension of Nelson's stochastic quantization to finite temperature using thermo field dynamics

    International Nuclear Information System (INIS)

    Kobayashi, K.; Yamanaka, Y.

    2011-01-01

    We present an extension of Nelson's stochastic quantum mechanics to finite temperature. Utilizing the formulation of Thermo Field Dynamics (TFD), we can show that Ito's stochastic equations for tilde and non-tilde particle positions reproduce the TFD-type Schroedinger equation which is equivalent to the Liouville-von Neumann equation. In our formalism, the drift terms in the Ito's stochastic equation have the temperature dependence and the thermal fluctuation is induced through the correlation of the non-tilde and tilde particles. We show that our formalism satisfies the position-momentum uncertainty relation at finite temperature. -- Highlights: → Utilizing TFD, we extend Nelson's stochastic method to finite temperature. → We introduce stochastic equations for tilde and non-tilde particles. → Our stochastic equations can reproduce the TFD-type Schroedinger equation. → Our formalism satisfies the uncertainly relation at finite temperature.

  8. Model selection for integrated pest management with stochasticity.

    Science.gov (United States)

    Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel

    2018-04-07

    In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. On the stress calculation within phase-field approaches: a model for finite deformations

    Science.gov (United States)

    Schneider, Daniel; Schwab, Felix; Schoof, Ephraim; Reiter, Andreas; Herrmann, Christoph; Selzer, Michael; Böhlke, Thomas; Nestler, Britta

    2017-08-01

    Numerical simulations based on phase-field methods are indispensable in order to investigate interesting and important phenomena in the evolution of microstructures. Microscopic phase transitions are highly affected by mechanical driving forces and therefore the accurate calculation of the stresses in the transition region is essential. We present a method for stress calculations within the phase-field framework, which satisfies the mechanical jump conditions corresponding to sharp interfaces, although the sharp interface is represented as a volumetric region using the phase-field approach. This model is formulated for finite deformations, is independent of constitutive laws, and allows using any type of phase inherent inelastic strains.

  10. Infinite-degree-corrected stochastic block model

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2014-01-01

    In stochastic block models, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statistically similar link probabilities within and between groups. A recent extension by Karrer and Newman [Karrer and Newman...... corrected stochastic block model as a nonparametric Bayesian model, incorporating a parameter to control the amount of degree correction that can then be inferred from data. Additionally, our formulation yields principled ways of inferring the number of groups as well as predicting missing links...

  11. A Comprehensive Model of the Near-Earth Magnetic Field. Phase 3

    Science.gov (United States)

    Sabaka, Terence J.; Olsen, Nils; Langel, Robert A.

    2000-01-01

    The near-Earth magnetic field is due to sources in Earth's core, ionosphere, magnetosphere, lithosphere, and from coupling currents between ionosphere and magnetosphere and between hemispheres. Traditionally, the main field (low degree internal field) and magnetospheric field have been modeled simultaneously, and fields from other sources modeled separately. Such a scheme, however, can introduce spurious features. A new model, designated CMP3 (Comprehensive Model: Phase 3), has been derived from quiet-time Magsat and POGO satellite measurements and observatory hourly and annual means measurements as part of an effort to coestimate fields from all of these sources. This model represents a significant advancement in the treatment of the aforementioned field sources over previous attempts, and includes an accounting for main field influences on the magnetosphere, main field and solar activity influences on the ionosphere, seasonal influences on the coupling currents, a priori characterization of ionospheric and magnetospheric influence on Earth-induced fields, and an explicit parameterization and estimation of the lithospheric field. The result of this effort is a model whose fits to the data are generally superior to previous models and whose parameter states for the various constituent sources are very reasonable.

  12. A Stochastic Model for Malaria Transmission Dynamics

    Directory of Open Access Journals (Sweden)

    Rachel Waema Mbogo

    2018-01-01

    Full Text Available Malaria is one of the three most dangerous infectious diseases worldwide (along with HIV/AIDS and tuberculosis. In this paper we compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in malaria transmission dynamics. Relationships between the basic reproduction number for malaria transmission dynamics between humans and mosquitoes and the extinction thresholds of corresponding continuous-time Markov chain models are derived under certain assumptions. The stochastic model is formulated using the continuous-time discrete state Galton-Watson branching process (CTDSGWbp. The reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or die out. Thresholds for disease extinction from stochastic models contribute crucial knowledge on disease control and elimination and mitigation of infectious diseases. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that malaria outbreak is more likely if the disease is introduced by infected mosquitoes as opposed to infected humans. These insights demonstrate the importance of a policy or intervention focusing on controlling the infected mosquito population if the control of malaria is to be realized.

  13. The generalized Fenyes-Nelson model for free scalar field theory

    International Nuclear Information System (INIS)

    Davidson, M.

    1980-01-01

    The generalized Fenyes-Nelson model of quantum mechanics is applied to the free scalar field. The resulting Markov field is equivalent to the Euclidean Markov field with the times scaled by a common factor which depends on the diffusion parameter. This result is consistent with Guerra's earlier work on stochastic quantization of scalar fields. It suggests a deep connection between Euclidean field theory and the stochastic interpretation of quantum mechanics. The question of Lorentz covariance is also discussed. (orig.)

  14. Classical and quantum stochastic models of resistive and memristive circuits

    Science.gov (United States)

    Gough, John E.; Zhang, Guofeng

    2017-07-01

    The purpose of this paper is to examine stochastic Markovian models for circuits in phase space for which the drift term is equivalent to the standard circuit equations. In particular, we include dissipative components corresponding to both a resistor and a memristor in series. We obtain a dilation of the problem which is canonical in the sense that the underlying Poisson bracket structure is preserved under the stochastic flow. We do this first of all for standard Wiener noise but also treat the problem using a new concept of symplectic noise, where the Poisson structure is extended to the noise as well as the circuit variables, and in particular where we have canonically conjugate noises. Finally, we construct a dilation which describes the quantum mechanical analogue.

  15. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  16. One-and two-dimensional topological charge distributions in stochastic optical fields

    CSIR Research Space (South Africa)

    Roux, FS

    2011-06-01

    Full Text Available The presentation on topological charge distributions in stochastic optical fields concludes that by using a combination of speckle fields one can produce inhomogeneous vortex distributions that allow both analytical calculations and numerical...

  17. Stochastic line motion and stochastic flux conservation for nonideal hydromagnetic models

    International Nuclear Information System (INIS)

    Eyink, Gregory L.

    2009-01-01

    We prove that smooth solutions of nonideal (viscous and resistive) incompressible magnetohydrodynamic (MHD) equations satisfy a stochastic law of flux conservation. This property implies that the magnetic flux through a surface is equal to the average of the magnetic fluxes through an ensemble of surfaces advected backward in time by the plasma velocity perturbed with a random white noise. Our result is an analog of the well-known Alfven theorem of ideal MHD and is valid for any value of the magnetic Prandtl number. A second stochastic conservation law is shown to hold at unit Prandtl number, a random version of the generalized Kelvin theorem derived by Bekenstein and Oron for ideal MHD. These stochastic conservation laws are not only shown to be consequences of the nonideal MHD equations but are proved in fact to be equivalent to those equations. We derive similar results for two more refined hydromagnetic models, Hall MHD and the two-fluid plasma model, still assuming incompressible velocities and isotropic transport coefficients. Finally, we use these results to discuss briefly the infinite-Reynolds-number limit of hydromagnetic turbulence and to support the conjecture that flux conservation remains stochastic in that limit.

  18. Solving stochastic inflation for arbitrary potentials

    International Nuclear Information System (INIS)

    Martin, Jerome; Musso, Marcello

    2006-01-01

    A perturbative method for solving the Langevin equation of inflationary cosmology in the presence of backreaction is presented. In the Gaussian approximation, the method permits an explicit calculation of the probability distribution of the inflaton field for an arbitrary potential, with or without the volume effects taken into account. The perturbative method is then applied to various concrete models, namely, large field, small field, hybrid, and running mass inflation. New results on the stochastic behavior of the inflaton field in those models are obtained. In particular, it is confirmed that the stochastic effects can be important in new inflation while it is demonstrated they are negligible in (vacuum dominated) hybrid inflation. The case of stochastic running mass inflation is discussed in some details and it is argued that quantum effects blur the distinction between the four classical versions of this model. It is also shown that the self-reproducing regime is likely to be important in this case

  19. A Navier-Stokes phase-field crystal model for colloidal suspensions.

    Science.gov (United States)

    Praetorius, Simon; Voigt, Axel

    2015-04-21

    We develop a fully continuous model for colloidal suspensions with hydrodynamic interactions. The Navier-Stokes Phase-Field Crystal model combines ideas of dynamic density functional theory with particulate flow approaches and is derived in detail and related to other dynamic density functional theory approaches with hydrodynamic interactions. The derived system is numerically solved using adaptive finite elements and is used to analyze colloidal crystallization in flowing environments demonstrating a strong coupling in both directions between the crystal shape and the flow field. We further validate the model against other computational approaches for particulate flow systems for various colloidal sedimentation problems.

  20. A stochastic model of enzyme kinetics

    Science.gov (United States)

    Stefanini, Marianne; Newman, Timothy; McKane, Alan

    2003-10-01

    Enzyme kinetics is generally modeled by deterministic rate equations, and in the simplest case leads to the well-known Michaelis-Menten equation. It is plausible that stochastic effects will play an important role at low enzyme concentrations. We have addressed this by constructing a simple stochastic model which can be exactly solved in the steady-state. Throughout a wide range of parameter values Michaelis-Menten dynamics is replaced by a new and simple theoretical result.

  1. Phase-field model of vapor-liquid-solid nanowire growth

    Science.gov (United States)

    Wang, Nan; Upmanyu, Moneesh; Karma, Alain

    2018-03-01

    We present a multiphase-field model to describe quantitatively nanowire growth by the vapor-liquid-solid (VLS) process. The free-energy functional of this model depends on three nonconserved order parameters that distinguish the vapor, liquid, and solid phases and describe the energetic properties of various interfaces, including arbitrary forms of anisotropic γ plots for the solid-vapor and solid-liquid interfaces. The evolution equations for those order parameters describe basic kinetic processes including the rapid (quasi-instantaneous) equilibration of the liquid catalyst to a droplet shape with constant mean curvature, the slow incorporation of growth atoms at the droplet surface, and crystallization within the droplet. The standard constraint that the sum of the phase fields equals unity and the conservation of the number of catalyst atoms, which relates the catalyst volume to the concentration of growth atoms inside the droplet, are handled via separate Lagrange multipliers. An analysis of the model is presented that rigorously maps the phase-field equations to a desired set of sharp-interface equations for the evolution of the phase boundaries under the constraint of force balance at three-phase junctions (triple points) given by the Young-Herring relation that includes torque term related to the anisotropy of the solid-liquid and solid-vapor interface excess free energies. Numerical examples of growth in two dimensions are presented for the simplest case of vanishing crystalline anisotropy and the more realistic case of a solid-liquid γ plot with cusped minima corresponding to two sets of (10 ) and (11 ) facets. The simulations reproduce many of the salient features of nanowire growth observed experimentally, including growth normal to the substrate with tapering of the side walls, transitions between different growth orientations, and crawling growth along the substrate. They also reproduce different observed relationships between the nanowire growth

  2. 5th Seminar on Stochastic Processes, Random Fields and Applications

    CERN Document Server

    Russo, Francesco; Dozzi, Marco

    2008-01-01

    This volume contains twenty-eight refereed research or review papers presented at the 5th Seminar on Stochastic Processes, Random Fields and Applications, which took place at the Centro Stefano Franscini (Monte Verità) in Ascona, Switzerland, from May 30 to June 3, 2005. The seminar focused mainly on stochastic partial differential equations, random dynamical systems, infinite-dimensional analysis, approximation problems, and financial engineering. The book will be a valuable resource for researchers in stochastic analysis and professionals interested in stochastic methods in finance. Contributors: Y. Asai, J.-P. Aubin, C. Becker, M. Benaïm, H. Bessaih, S. Biagini, S. Bonaccorsi, N. Bouleau, N. Champagnat, G. Da Prato, R. Ferrière, F. Flandoli, P. Guasoni, V.B. Hallulli, D. Khoshnevisan, T. Komorowski, R. Léandre, P. Lescot, H. Lisei, J.A. López-Mimbela, V. Mandrekar, S. Méléard, A. Millet, H. Nagai, A.D. Neate, V. Orlovius, M. Pratelli, N. Privault, O. Raimond, M. Röckner, B. Rüdiger, W.J. Runggaldi...

  3. Stochastic Control - External Models

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad

    2005-01-01

    This note is devoted to control of stochastic systems described in discrete time. We are concerned with external descriptions or transfer function model, where we have a dynamic model for the input output relation only (i.e.. no direct internal information). The methods are based on LTI systems...

  4. Stochastic dynamics and irreversibility

    CERN Document Server

    Tomé, Tânia

    2015-01-01

    This textbook presents an exposition of stochastic dynamics and irreversibility. It comprises the principles of probability theory and the stochastic dynamics in continuous spaces, described by Langevin and Fokker-Planck equations, and in discrete spaces, described by Markov chains and master equations. Special concern is given to the study of irreversibility, both in systems that evolve to equilibrium and in nonequilibrium stationary states. Attention is also given to the study of models displaying phase transitions and critical phenomema both in thermodynamic equilibrium and out of equilibrium. These models include the linear Glauber model, the Glauber-Ising model, lattice models with absorbing states such as the contact process and those used in population dynamic and spreading of epidemic, probabilistic cellular automata, reaction-diffusion processes, random sequential adsorption and dynamic percolation. A stochastic approach to chemical reaction is also presented.The textbook is intended for students of ...

  5. Stochastic dynamic modeling of regular and slow earthquakes

    Science.gov (United States)

    Aso, N.; Ando, R.; Ide, S.

    2017-12-01

    Both regular and slow earthquakes are slip phenomena on plate boundaries and are simulated by a (quasi-)dynamic modeling [Liu and Rice, 2005]. In these numerical simulations, spatial heterogeneity is usually considered not only for explaining real physical properties but also for evaluating the stability of the calculations or the sensitivity of the results on the condition. However, even though we discretize the model space with small grids, heterogeneity at smaller scales than the grid size is not considered in the models with deterministic governing equations. To evaluate the effect of heterogeneity at the smaller scales we need to consider stochastic interactions between slip and stress in a dynamic modeling. Tidal stress is known to trigger or affect both regular and slow earthquakes [Yabe et al., 2015; Ide et al., 2016], and such an external force with fluctuation can also be considered as a stochastic external force. A healing process of faults may also be stochastic, so we introduce stochastic friction law. In the present study, we propose a stochastic dynamic model to explain both regular and slow earthquakes. We solve mode III problem, which corresponds to the rupture propagation along the strike direction. We use BIEM (boundary integral equation method) scheme to simulate slip evolution, but we add stochastic perturbations in the governing equations, which is usually written in a deterministic manner. As the simplest type of perturbations, we adopt Gaussian deviations in the formulation of the slip-stress kernel, external force, and friction. By increasing the amplitude of perturbations of the slip-stress kernel, we reproduce complicated rupture process of regular earthquakes including unilateral and bilateral ruptures. By perturbing external force, we reproduce slow rupture propagation at a scale of km/day. The slow propagation generated by a combination of fast interaction at S-wave velocity is analogous to the kinetic theory of gasses: thermal

  6. The stochastic spectator

    Energy Technology Data Exchange (ETDEWEB)

    Hardwick, Robert J.; Vennin, Vincent; Wands, David [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX (United Kingdom); Byrnes, Christian T.; Torrado, Jesús, E-mail: robert.hardwick@port.ac.uk, E-mail: vincent.vennin@port.ac.uk, E-mail: c.byrnes@sussex.ac.uk, E-mail: jesus.torrado@sussex.ac.uk, E-mail: david.wands@port.ac.uk [Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH (United Kingdom)

    2017-10-01

    We study the stochastic distribution of spectator fields predicted in different slow-roll inflation backgrounds. Spectator fields have a negligible energy density during inflation but may play an important dynamical role later, even giving rise to primordial density perturbations within our observational horizon today. During de-Sitter expansion there is an equilibrium solution for the spectator field which is often used to estimate the stochastic distribution during slow-roll inflation. However slow roll only requires that the Hubble rate varies slowly compared to the Hubble time, while the time taken for the stochastic distribution to evolve to the de-Sitter equilibrium solution can be much longer than a Hubble time. We study both chaotic (monomial) and plateau inflaton potentials, with quadratic, quartic and axionic spectator fields. We give an adiabaticity condition for the spectator field distribution to relax to the de-Sitter equilibrium, and find that the de-Sitter approximation is never a reliable estimate for the typical distribution at the end of inflation for a quadratic spectator during monomial inflation. The existence of an adiabatic regime at early times can erase the dependence on initial conditions of the final distribution of field values. In these cases, spectator fields acquire sub-Planckian expectation values. Otherwise spectator fields may acquire much larger field displacements than suggested by the de-Sitter equilibrium solution. We quantify the information about initial conditions that can be obtained from the final field distribution. Our results may have important consequences for the viability of spectator models for the origin of structure, such as the simplest curvaton models.

  7. The stochastic spectator

    International Nuclear Information System (INIS)

    Hardwick, Robert J.; Vennin, Vincent; Wands, David; Byrnes, Christian T.; Torrado, Jesús

    2017-01-01

    We study the stochastic distribution of spectator fields predicted in different slow-roll inflation backgrounds. Spectator fields have a negligible energy density during inflation but may play an important dynamical role later, even giving rise to primordial density perturbations within our observational horizon today. During de-Sitter expansion there is an equilibrium solution for the spectator field which is often used to estimate the stochastic distribution during slow-roll inflation. However slow roll only requires that the Hubble rate varies slowly compared to the Hubble time, while the time taken for the stochastic distribution to evolve to the de-Sitter equilibrium solution can be much longer than a Hubble time. We study both chaotic (monomial) and plateau inflaton potentials, with quadratic, quartic and axionic spectator fields. We give an adiabaticity condition for the spectator field distribution to relax to the de-Sitter equilibrium, and find that the de-Sitter approximation is never a reliable estimate for the typical distribution at the end of inflation for a quadratic spectator during monomial inflation. The existence of an adiabatic regime at early times can erase the dependence on initial conditions of the final distribution of field values. In these cases, spectator fields acquire sub-Planckian expectation values. Otherwise spectator fields may acquire much larger field displacements than suggested by the de-Sitter equilibrium solution. We quantify the information about initial conditions that can be obtained from the final field distribution. Our results may have important consequences for the viability of spectator models for the origin of structure, such as the simplest curvaton models.

  8. Dynamics of a Stochastic Intraguild Predation Model

    Directory of Open Access Journals (Sweden)

    Zejing Xing

    2016-04-01

    Full Text Available Intraguild predation (IGP is a widespread ecological phenomenon which occurs when one predator species attacks another predator species with which it competes for a shared prey species. The objective of this paper is to study the dynamical properties of a stochastic intraguild predation model. We analyze stochastic persistence and extinction of the stochastic IGP model containing five cases and establish the sufficient criteria for global asymptotic stability of the positive solutions. This study shows that it is possible for the coexistence of three species under the influence of environmental noise, and that the noise may have a positive effect for IGP species. A stationary distribution of the stochastic IGP model is established and it has the ergodic property, suggesting that the time average of population size with the development of time is equal to the stationary distribution in space. Finally, we show that our results may be extended to two well-known biological systems: food chains and exploitative competition.

  9. Stochastic mean-field dynamics for fermions in the weak coupling limit

    International Nuclear Information System (INIS)

    Lacroix, D.

    2005-09-01

    Assuming that the effect of the residual interaction beyond mean-field is weak and can be treated as a statistical ensemble of two-body interactions, a Markovian quantum jump theory is developed for fermionic systems. In this theory, jumps occur between many-body densities formed of pairs of states D |Φ a > b | / b | |Φ a > where |Φ a > and |Φ b > are anti-symmetrized products of single-particle states. The underlying Stochastic Mean-Field (SMF) theory is discussed and applied to the monopole vibration of a spherical 40 Ca nucleus under the influence of a statistical ensemble of two-body contact interactions. In this example, the mean-field evolution of one-body observables is recovered by averaging over different stochastic trajectories while fluctuations beyond mean-field are observed. Finally, the nature of the fluctuations is discussed. (author)

  10. Stochastic quantum inflation for a canonical scalar field with linear self-interaction potential

    Energy Technology Data Exchange (ETDEWEB)

    Panotopoulos, Grigoris [CENTRA, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa (Portugal)

    2017-10-15

    We apply Starobinsky's formalism of stochastic inflation to the case of a massless minimally coupled scalar field with linear self-interaction potential. We solve the corresponding Fokker-Planck equation exactly, and we obtain analytical expressions for the stochastic expectation values. (orig.)

  11. Phase-Field Modeling of Polycrystalline Solidification: From Needle Crystals to Spherulites—A Review

    Science.gov (United States)

    Gránásy, László; Rátkai, László; Szállás, Attila; Korbuly, Bálint; Tóth, Gyula I.; Környei, László; Pusztai, Tamás

    2014-04-01

    Advances in the orientation-field-based phase-field (PF) models made in the past are reviewed. The models applied incorporate homogeneous and heterogeneous nucleation of growth centers and several mechanisms to form new grains at the perimeter of growing crystals, a phenomenon termed growth front nucleation. Examples for PF modeling of such complex polycrystalline structures are shown as impinging symmetric dendrites, polycrystalline growth forms (ranging from disordered dendrites to spherulitic patterns), and various eutectic structures, including spiraling two-phase dendrites. Simulations exploring possible control of solidification patterns in thin films via external fields, confined geometry, particle additives, scratching/piercing the films, etc. are also displayed. Advantages, problems, and possible solutions associated with quantitative PF simulations are discussed briefly.

  12. Stochastic flow modeling : Quasi-Geostrophy, Taylor state and torsional wave excitation

    DEFF Research Database (Denmark)

    Gillet, Nicolas; Jault, D.; Finlay, Chris

    We reconstruct the core flow evolution over the period 1840-2010 under the quasi-geostrophic assumption, from the stochastic magnetic field model COV-OBS and its full model error covariance matrix. We make use of a prior information on the flow temporal power spectrum compatible with that of obse......We reconstruct the core flow evolution over the period 1840-2010 under the quasi-geostrophic assumption, from the stochastic magnetic field model COV-OBS and its full model error covariance matrix. We make use of a prior information on the flow temporal power spectrum compatible....... Large length-scales flow features are naturally dominated by their equatorially symmetric component from about 1900 when the symmetry constraint is relaxed. Equipartition of the kinetic energy in both symmetries coincides with the poor prediction of decadal length-of-day changes in the XIXth century. We...... interpret this as an evidence for quasi-geostrophic rapid flow changes, and the consequence of a too loose data constraint during the oldest period. We manage to retrieve rapid flow changes over the past 60 yrs, and in particular modulated torsional waves predicting correctly interannual length-of day...

  13. Stochastic Modeling of Traffic Air Pollution

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    2014-01-01

    In this paper, modeling of traffic air pollution is discussed with special reference to infrastructures. A number of subjects related to health effects of air pollution and the different types of pollutants are briefly presented. A simple model for estimating the social cost of traffic related air...... and using simple Monte Carlo techniques to obtain a stochastic estimate of the costs of traffic air pollution for infrastructures....... pollution is derived. Several authors have published papers on this very complicated subject, but no stochastic modelling procedure have obtained general acceptance. The subject is discussed basis of a deterministic model. However, it is straightforward to modify this model to include uncertain parameters...

  14. Stochastic modelling of turbulence

    DEFF Research Database (Denmark)

    Sørensen, Emil Hedevang Lohse

    previously been shown to be closely connected to the energy dissipation. The incorporation of the small scale dynamics into the spatial model opens the door to a fully fledged stochastic model of turbulence. Concerning the interaction of wind and wind turbine, a new method is proposed to extract wind turbine...

  15. Continuum modeling of ion-beam eroded surfaces under normal incidence: Impact of stochastic fluctuations

    International Nuclear Information System (INIS)

    Dreimann, Karsten; Linz, Stefan J.

    2010-01-01

    Graphical abstract: Deterministic surface pattern (left) and its stochastic counterpart (right) arising in a stochastic damped Kuramoto-Sivashinsky equation that serves as a model equation for ion-beam eroded surfaces and is systematically investigated. - Abstract: Using a recently proposed field equation for the surface evolution of ion-beam eroded semiconductor target materials under normal incidence, we systematically explore the impact of additive stochastic fluctuations that are permanently present during the erosion process. Specifically, we investigate the dependence of the surface roughness, the underlying pattern forming properties and the bifurcation behavior on the strength of the fluctuations.

  16. Expansion of a stochastic stationary optical field at a fixed point

    International Nuclear Information System (INIS)

    Martinez-Herrero, R.; Mejias, P.M.

    1984-01-01

    An important problem in single and multifold photoelectron statistics is to determine the statistical properties of a totally polarized optical field at some point →r from the photoelectron counts registered by the detector. The solution to this problem may be found in the determination of the statistical properties of an integral over a stochastic process; a complicated and formidable task. This problem can be solved in some cases of interest by expanding the process V(t) (which represents the field at →r) in a set of complete orthonormal deterministic functions, resulting in the so-called Karhunen-Loeve expansion of V(t). Two disadvantages are that the process must be defined over a finite time interval, and that each term of the series does not represent any special optical field. Taking into account these limitations of the expansion, the purpose of this work is to find another alternative expansion of stationary optical fields defined over the infinite time interval, and whose terms represent stochastic fields

  17. Sampling from stochastic reservoir models constrained by production data

    Energy Technology Data Exchange (ETDEWEB)

    Hegstad, Bjoern Kaare

    1997-12-31

    When a petroleum reservoir is evaluated, it is important to forecast future production of oil and gas and to assess forecast uncertainty. This is done by defining a stochastic model for the reservoir characteristics, generating realizations from this model and applying a fluid flow simulator to the realizations. The reservoir characteristics define the geometry of the reservoir, initial saturation, petrophysical properties etc. This thesis discusses how to generate realizations constrained by production data, that is to say, the realizations should reproduce the observed production history of the petroleum reservoir within the uncertainty of these data. The topics discussed are: (1) Theoretical framework, (2) History matching, forecasting and forecasting uncertainty, (3) A three-dimensional test case, (4) Modelling transmissibility multipliers by Markov random fields, (5) Up scaling, (6) The link between model parameters, well observations and production history in a simple test case, (7) Sampling the posterior using optimization in a hierarchical model, (8) A comparison of Rejection Sampling and Metropolis-Hastings algorithm, (9) Stochastic simulation and conditioning by annealing in reservoir description, and (10) Uncertainty assessment in history matching and forecasting. 139 refs., 85 figs., 1 tab.

  18. Effects of stochastic time-delayed feedback on a dynamical system modeling a chemical oscillator

    Science.gov (United States)

    González Ochoa, Héctor O.; Perales, Gualberto Solís; Epstein, Irving R.; Femat, Ricardo

    2018-05-01

    We examine how stochastic time-delayed negative feedback affects the dynamical behavior of a model oscillatory reaction. We apply constant and stochastic time-delayed negative feedbacks to a point Field-Körös-Noyes photosensitive oscillator and compare their effects. Negative feedback is applied in the form of simulated inhibitory electromagnetic radiation with an intensity proportional to the concentration of oxidized light-sensitive catalyst in the oscillator. We first characterize the system under nondelayed inhibitory feedback; then we explore and compare the effects of constant (deterministic) versus stochastic time-delayed feedback. We find that the oscillatory amplitude, frequency, and waveform are essentially preserved when low-dispersion stochastic delayed feedback is used, whereas small but measurable changes appear when a large dispersion is applied.

  19. Parameter estimation in stochastic rainfall-runoff models

    DEFF Research Database (Denmark)

    Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

    2006-01-01

    A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

  20. Test models for improving filtering with model errors through stochastic parameter estimation

    International Nuclear Information System (INIS)

    Gershgorin, B.; Harlim, J.; Majda, A.J.

    2010-01-01

    The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

  1. Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

    Energy Technology Data Exchange (ETDEWEB)

    Thimmisetty, Charanraj A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Zhao, Wenju [Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing; Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Tong, Charles H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; White, Joshua A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth and Energy Division

    2017-10-18

    Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.

  2. Modeling of Focused Acoustic Field of a Concave Multi-annular Phased Array Using Spheroidal Beam Equation

    Science.gov (United States)

    Yu, Li-Li; Shou, Wen-De; Hui, Chun

    2012-02-01

    A theoretical model of focused acoustic field for a multi-annular phased array on concave spherical surface is proposed. In this model, the source boundary conditions of the spheroidal beam equation (SBE) for multi-annular phased elements are studied. Acoustic field calculated by the dynamic focusing model of SBE is compared with numerical results of the O'Neil and Khokhlov—Zabolotskaya—Kuznetsov (KZK) model, respectively. Axial dynamic focusing and the harmonic effects are presented. The results demonstrate that the dynamic focusing model of SBE is good valid for a concave multi-annular phased array with a large aperture angle in the linear or nonlinear field.

  3. 12th Workshop on Stochastic Models, Statistics and Their Applications

    CERN Document Server

    Rafajłowicz, Ewaryst; Szajowski, Krzysztof

    2015-01-01

    This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

  4. Diffusion of test particles in stochastic magnetic fields in the percolative regime

    International Nuclear Information System (INIS)

    Neuer, Marcus; Spatschek, Karl H.

    2006-01-01

    For stochastic magnetic flux functions with percolative contours the test particle transport is investigated. The calculations make use of the stochastic Liouville approach. They start from the so-called A-Langevin equations, including stochastic magnetic field components and binary collisions. Using the decorrelation trajectory method, a relation between the Lagrangian velocity correlation function and the Eulerian magnetic field correlation is derived and introduced into the Green-Kubo formalism. Finite Larmor radius effects are included. Interesting results are presented in the percolation regime corresponding to high Kubo numbers. Previous results are found to be limiting cases for small Kubo numbers. For different percolative scenarios the diffusion is analyzed and strong influences of the percolative structures on the transport scaling are found. The finite Larmor radius effects are discussed in detail. Numerical simulations of the A-Langevin equation confirm the semianalytical predictions

  5. Noisy mean field game model for malware propagation in opportunistic networks

    KAUST Repository

    Tembine, Hamidou

    2012-01-01

    In this paper we present analytical mean field techniques that can be used to better understand the behavior of malware propagation in opportunistic large networks. We develop a modeling methodology based on stochastic mean field optimal control that is able to capture many aspects of the problem, especially the impact of the control and heterogeneity of the system on the spreading characteristics of malware. The stochastic large process characterizing the evolution of the total number of infected nodes is examined with a noisy mean field limit and compared to a deterministic one. The stochastic nature of the wireless environment make stochastic approaches more realistic for such types of networks. By introducing control strategies, we show that the fraction of infected nodes can be maintained below some threshold. In contrast to most of the existing results on mean field propagation models which focus on deterministic equations, we show that the mean field limit is stochastic if the second moment of the number of object transitions per time slot is unbounded with the size of the system. This allows us to compare one path of the fraction of infected nodes with the stochastic trajectory of its mean field limit. In order to take into account the heterogeneity of opportunistic networks, the analysis is extended to multiple types of nodes. Our numerical results show that the heterogeneity can help to stabilize the system. We verify the results through simulation showing how to obtain useful approximations in the case of very large systems. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

  6. Stochastic mean-field dynamics for fermions in the weak coupling limit

    Energy Technology Data Exchange (ETDEWEB)

    Lacroix, D

    2005-09-15

    Assuming that the effect of the residual interaction beyond mean-field is weak and can be treated as a statistical ensemble of two-body interactions, a Markovian quantum jump theory is developed for fermionic systems. In this theory, jumps occur between many-body densities formed of pairs of states D |{phi}{sub a}> <|{phi}{sub b}| / <|{phi}{sub b} | |{phi} {sub a}> where |{phi}{sub a}> and |{phi}{sub b}> are anti-symmetrized products of single-particle states. The underlying Stochastic Mean-Field (SMF) theory is discussed and applied to the monopole vibration of a spherical {sup 40}Ca nucleus under the influence of a statistical ensemble of two-body contact interactions. In this example, the mean-field evolution of one-body observables is recovered by averaging over different stochastic trajectories while fluctuations beyond mean-field are observed. Finally, the nature of the fluctuations is discussed. (author)

  7. The Rapid Evaluation of Mean Concentration Fields in Lagrangian Stochastic Modelling Using a Density Kernel Estimator

    National Research Council Canada - National Science Library

    Shao, Y

    2004-01-01

    Lagrangian Stochastic (LS) particle models have proven to be a useful computational tool for the description and prediction of dispersion of pollutant releases in complex meteorological situations (e.g...

  8. Environmental versus demographic variability in stochastic predator–prey models

    International Nuclear Information System (INIS)

    Dobramysl, U; Täuber, U C

    2013-01-01

    In contrast to the neutral population cycles of the deterministic mean-field Lotka–Volterra rate equations, including spatial structure and stochastic noise in models for predator–prey interactions yields complex spatio-temporal structures associated with long-lived erratic population oscillations. Environmental variability in the form of quenched spatial randomness in the predation rates results in more localized activity patches. Our previous study showed that population fluctuations in rare favorable regions in turn cause a remarkable increase in the asymptotic densities of both predators and prey. Very intriguing features are found when variable interaction rates are affixed to individual particles rather than lattice sites. Stochastic dynamics with demographic variability in conjunction with inheritable predation efficiencies generate non-trivial time evolution for the predation rate distributions, yet with overall essentially neutral optimization. (paper)

  9. Constraining Stochastic Parametrisation Schemes Using High-Resolution Model Simulations

    Science.gov (United States)

    Christensen, H. M.; Dawson, A.; Palmer, T.

    2017-12-01

    Stochastic parametrisations are used in weather and climate models as a physically motivated way to represent model error due to unresolved processes. Designing new stochastic schemes has been the target of much innovative research over the last decade. While a focus has been on developing physically motivated approaches, many successful stochastic parametrisation schemes are very simple, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) multiplicative scheme `Stochastically Perturbed Parametrisation Tendencies' (SPPT). The SPPT scheme improves the skill of probabilistic weather and seasonal forecasts, and so is widely used. However, little work has focused on assessing the physical basis of the SPPT scheme. We address this matter by using high-resolution model simulations to explicitly measure the `error' in the parametrised tendency that SPPT seeks to represent. The high resolution simulations are first coarse-grained to the desired forecast model resolution before they are used to produce initial conditions and forcing data needed to drive the ECMWF Single Column Model (SCM). By comparing SCM forecast tendencies with the evolution of the high resolution model, we can measure the `error' in the forecast tendencies. In this way, we provide justification for the multiplicative nature of SPPT, and for the temporal and spatial scales of the stochastic perturbations. However, we also identify issues with the SPPT scheme. It is therefore hoped these measurements will improve both holistic and process based approaches to stochastic parametrisation. Figure caption: Instantaneous snapshot of the optimal SPPT stochastic perturbation, derived by comparing high-resolution simulations with a low resolution forecast model.

  10. Experimental evidence for stochastic switching of supercooled phases in NdNiO3 nanostructures

    Science.gov (United States)

    Kumar, Devendra; Rajeev, K. P.; Alonso, J. A.

    2018-03-01

    A first-order phase transition is a dynamic phenomenon. In a multi-domain system, the presence of multiple domains of coexisting phases averages out the dynamical effects, making it nearly impossible to predict the exact nature of phase transition dynamics. Here, we report the metal-insulator transition in samples of sub-micrometer size NdNiO3 where the effect of averaging is minimized by restricting the number of domains under study. We observe the presence of supercooled metallic phases with supercooling of 40 K or more. The transformation from the supercooled metallic to the insulating state is a stochastic process that happens at different temperatures and times in different experimental runs. The experimental results are understood without incorporating material specific properties, suggesting that the behavior is of universal nature. The size of the sample needed to observe individual switching of supercooled domains, the degree of supercooling, and the time-temperature window of switching are expected to depend on the parameters such as quenched disorder, strain, and magnetic field.

  11. Born's reciprocity principle in stochastic phase space

    International Nuclear Information System (INIS)

    Prugovecki, E.

    1981-01-01

    It is shown that the application of Born's reciprocity principle to relativistic quantum mechanics in stochastic phase space (by the requirement that the proper wave functions of extended particles satisfy the Born-Lande as well as the Klein-Gordon equation) leads to the unique determination of these functions for any given value of their rms radius. The resulting particle propagators display not only Lorentz but also reciprocal invariance. This feature remains true even in the case of mass-zero particles, such as photons, when their localization is achieved by means of extended test particles whose proper wave functions obey the reciprocity principle. (author)

  12. A stochastic modeling of recurrent measles epidemic | Kassem ...

    African Journals Online (AJOL)

    A simple stochastic mathematical model is developed and investigated for the dynamics of measles epidemic. The model, which is a multi-dimensional diffusion process, includes susceptible individuals, latent (exposed), infected and removed individuals. Stochastic effects are assumed to arise in the process of infection of ...

  13. Stochastic quantization for the axial model

    International Nuclear Information System (INIS)

    Farina, C.; Montani, H.; Albuquerque, L.C.

    1991-01-01

    We use bosonization ideas to solve the axial model in the stochastic quantization framework. We obtain the fermion propagator of the theory decoupling directly the Langevin equation, instead of the Fokker-Planck equation. In the Appendix we calculate explicitly the anomalous divergence of the axial-vector current by using a regularization that does not break the Markovian character of the stochastic process

  14. Stochastic model simulation using Kronecker product analysis and Zassenhaus formula approximation.

    Science.gov (United States)

    Caglar, Mehmet Umut; Pal, Ranadip

    2013-01-01

    Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Probabilistic Boolean Networks have been proposed to model the stochastic behavior in genetic regulatory networks. It is generally accepted that Stochastic Master Equation is a fundamental model that can describe the system being investigated in fine detail, but the application of this model is computationally enormously expensive. On the other hand, Probabilistic Boolean Network captures only the coarse-scale stochastic properties of the system without modeling the detailed interactions. We propose a new approximation of the stochastic master equation model that is able to capture the finer details of the modeled system including bistabilities and oscillatory behavior, and yet has a significantly lower computational complexity. In this new method, we represent the system using tensors and derive an identity to exploit the sparse connectivity of regulatory targets for complexity reduction. The algorithm involves an approximation based on Zassenhaus formula to represent the exponential of a sum of matrices as product of matrices. We derive upper bounds on the expected error of the proposed model distribution as compared to the stochastic master equation model distribution. Simulation results of the application of the model to four different biological benchmark systems illustrate performance comparable to detailed stochastic master equation models but with considerably lower computational complexity. The results also demonstrate the reduced complexity of the new approach as compared to commonly used Stochastic Simulation Algorithm for equivalent accuracy.

  15. Analysis of stochastic effects in Kaldor-type business cycle discrete model

    Science.gov (United States)

    Bashkirtseva, Irina; Ryashko, Lev; Sysolyatina, Anna

    2016-07-01

    We study nonlinear stochastic phenomena in the discrete Kaldor model of business cycles. A numerical parametric analysis of stochastically forced attractors (equilibria, closed invariant curves, discrete cycles) of this model is performed using the stochastic sensitivity functions technique. A spatial arrangement of random states in stochastic attractors is modeled by confidence domains. The phenomenon of noise-induced transitions ;chaos-order; is discussed.

  16. Modeling of Focused Acoustic Field of a Concave Multi-annular Phased Array Using Spheroidal Beam Equation

    International Nuclear Information System (INIS)

    Yu Lili; Shou Wende; Hui Chun

    2012-01-01

    A theoretical model of focused acoustic field for a multi-annular phased array on concave spherical surface is proposed. In this model, the source boundary conditions of the spheroidal beam equation (SBE) for multi-annular phased elements are studied. Acoustic field calculated by the dynamic focusing model of SBE is compared with numerical results of the O'Neil and Khokhlov-Zabolotskaya-Kuznetsov (KZK) model, respectively. Axial dynamic focusing and the harmonic effects are presented. The results demonstrate that the dynamic focusing model of SBE is good valid for a concave multi-annular phased array with a large aperture angle in the linear or nonlinear field. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  17. Stochastic Spectral Descent for Discrete Graphical Models

    International Nuclear Information System (INIS)

    Carlson, David; Hsieh, Ya-Ping; Collins, Edo; Carin, Lawrence; Cevher, Volkan

    2015-01-01

    Interest in deep probabilistic graphical models has in-creased in recent years, due to their state-of-the-art performance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a significant number of iterations to converge. Since the computational cost of gradient estimation is prohibitive even for modestly sized models, training becomes slow and practically usable models are kept small. In this paper we propose a new, largely tuning-free algorithm to address this problem. Our approach derives novel majorization bounds based on the Schatten- norm. Intriguingly, the minimizers of these bounds can be interpreted as gradient methods in a non-Euclidean space. We thus propose using a stochastic gradient method in non-Euclidean space. We both provide simple conditions under which our algorithm is guaranteed to converge, and demonstrate empirically that our algorithm leads to dramatically faster training and improved predictive ability compared to stochastic gradient descent for both directed and undirected graphical models.

  18. Modeling cytoskeletal flow over adhesion sites: competition between stochastic bond dynamics and intracellular relaxation

    International Nuclear Information System (INIS)

    Sabass, Benedikt; Schwarz, Ulrich S

    2010-01-01

    In migrating cells, retrograde flow of the actin cytoskeleton is related to traction at adhesion sites located at the base of the lamellipodium. The coupling between the moving cytoskeleton and the stationary adhesions is mediated by the continuous association and dissociation of molecular bonds. We introduce a simple model for the competition between the stochastic dynamics of elastic bonds at the moving interface and relaxation within the moving actin cytoskeleton represented by an internal viscous friction coefficient. Using exact stochastic simulations and an analytical mean field theory, we show that the stochastic bond dynamics lead to biphasic friction laws as observed experimentally. At low internal dissipation, stochastic bond dynamics lead to a regime of irregular stick-and-slip motion. High internal dissipation effectively suppresses cooperative effects among bonds and hence stabilizes the adhesion.

  19. An application of information theory to stochastic classical gravitational fields

    Science.gov (United States)

    Angulo, J.; Angulo, J. C.; Angulo, J. M.

    2018-06-01

    The objective of this study lies on the incorporation of the concepts developed in the Information Theory (entropy, complexity, etc.) with the aim of quantifying the variation of the uncertainty associated with a stochastic physical system resident in a spatiotemporal region. As an example of application, a relativistic classical gravitational field has been considered, with a stochastic behavior resulting from the effect induced by one or several external perturbation sources. One of the key concepts of the study is the covariance kernel between two points within the chosen region. Using this concept and the appropriate criteria, a methodology is proposed to evaluate the change of uncertainty at a given spatiotemporal point, based on available information and efficiently applying the diverse methods that Information Theory provides. For illustration, a stochastic version of the Einstein equation with an added Gaussian Langevin term is analyzed.

  20. A comprehensive model of the quiet-time, near-Earth magnetic field: phase 3

    DEFF Research Database (Denmark)

    Sabaka, T.J.; Olsen, Nils; Langel, R.A.

    2002-01-01

    been modelled simultaneously, with fields from other sources being modelled separately. Such a scheme, however, can introduce spurious features, especially when the spatial and temporal scales of the fields overlap. A new model, designated CM3 (Comprehensive Model: phase 3), is the third in a series...... of efforts to coestimate fields from all of these sources. This model has been derived from quiet-time Magsat and POGO satellite and observatory hourly means measurements for the period 1960-1985. It represents a significant advance in the treatment of the aforementioned field sources over previous attempts...... parametrization and estimation of the lithospheric field. The result is a model that describes well the 591 432 data with 16 594 parameters, implying a data-to-parameter ratio of 36, which is larger than several popular field models....

  1. Stochastic fractional differential equations: Modeling, method and analysis

    International Nuclear Information System (INIS)

    Pedjeu, Jean-C.; Ladde, Gangaram S.

    2012-01-01

    By introducing a concept of dynamic process operating under multi-time scales in sciences and engineering, a mathematical model described by a system of multi-time scale stochastic differential equations is formulated. The classical Picard–Lindelöf successive approximations scheme is applied to the model validation problem, namely, existence and uniqueness of solution process. Naturally, this leads to the problem of finding closed form solutions of both linear and nonlinear multi-time scale stochastic differential equations of Itô–Doob type. Finally, to illustrate the scope of ideas and presented results, multi-time scale stochastic models for ecological and epidemiological processes in population dynamic are outlined.

  2. Tsunamis: stochastic models of occurrence and generation mechanisms

    Science.gov (United States)

    Geist, Eric L.; Oglesby, David D.

    2014-01-01

    The devastating consequences of the 2004 Indian Ocean and 2011 Japan tsunamis have led to increased research into many different aspects of the tsunami phenomenon. In this entry, we review research related to the observed complexity and uncertainty associated with tsunami generation, propagation, and occurrence described and analyzed using a variety of stochastic methods. In each case, seismogenic tsunamis are primarily considered. Stochastic models are developed from the physical theories that govern tsunami evolution combined with empirical models fitted to seismic and tsunami observations, as well as tsunami catalogs. These stochastic methods are key to providing probabilistic forecasts and hazard assessments for tsunamis. The stochastic methods described here are similar to those described for earthquakes (Vere-Jones 2013) and volcanoes (Bebbington 2013) in this encyclopedia.

  3. An extension of clarke's model with stochastic amplitude flip processes

    KAUST Repository

    Hoel, Hakon

    2014-07-01

    Stochastic modeling is an essential tool for studying statistical properties of wireless channels. In multipath fading channel (MFC) models, the signal reception is modeled by a sum of wave path contributions, and Clarke\\'s model is an important example of such which has been widely accepted in many wireless applications. However, since Clarke\\'s model is temporally deterministic, Feng and Field noted that it does not model real wireless channels with time-varying randomness well. Here, we extend Clarke\\'s model to a novel time-varying stochastic MFC model with scatterers randomly flipping on and off. Statistical properties of the MFC model are analyzed and shown to fit well with real signal measurements, and a limit Gaussian process is derived from the model when the number of active wave paths tends to infinity. A second focus of this work is a comparison study of the error and computational cost of generating signal realizations from the MFC model and from its limit Gaussian process. By rigorous analysis and numerical studies, we show that in many settings, signal realizations are generated more efficiently by Gaussian process algorithms than by the MFC model\\'s algorithm. Numerical examples that strengthen these observations are also presented. © 2014 IEEE.

  4. ARIMA-Based Time Series Model of Stochastic Wind Power Generation

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Pedersen, Troels; Bak-Jensen, Birgitte

    2010-01-01

    This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from...... the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation...... and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power...

  5. Phase transitions and self-organized criticality in networks of stochastic spiking neurons.

    Science.gov (United States)

    Brochini, Ludmila; de Andrade Costa, Ariadne; Abadi, Miguel; Roque, Antônio C; Stolfi, Jorge; Kinouchi, Osame

    2016-11-07

    Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains - a form of short-term plasticity probably located at the axon initial segment (AIS) - instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.

  6. Quantum field theory and phase transitions: universality and renormalization group

    International Nuclear Information System (INIS)

    Zinn-Justin, J.

    2003-08-01

    In the quantum field theory the problem of infinite values has been solved empirically through a method called renormalization, this method is satisfying only in the framework of renormalization group. It is in the domain of statistical physics and continuous phase transitions that these issues are the easiest to discuss. Within the framework of a course in theoretical physics the author introduces the notions of continuous limits and universality in stochastic systems operating with a high number of freedom degrees. It is shown that quasi-Gaussian and mean field approximation are unable to describe phase transitions in a satisfying manner. A new concept is required: it is the notion of renormalization group whose fixed points allow us to understand universality beyond mean field. The renormalization group implies the idea that long distance correlations near the transition temperature might be described by a statistical field theory that is a quantum field in imaginary time. Various forms of renormalization group equations are presented and solved in particular boundary limits, namely for fields with high numbers of components near the dimensions 4 and 2. The particular case of exact renormalization group is also introduced. (A.C.)

  7. Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors

    DEFF Research Database (Denmark)

    Del Giudice, Dario; Löwe, Roland; Madsen, Henrik

    2015-01-01

    from different fields and have not yet been compared in environmental modeling. To compare the two approaches, we develop a unifying terminology, evaluate them theoretically, and apply them to conceptual rainfall-runoff modeling in the same drainage system. Our results show that both approaches can......In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two...... approaches which use stochastic processes to describe systematic deviations and to discuss their advantages and drawbacks for urban drainage modeling. The two methodologies are an external bias description (EBD) and an internal noise description (IND, also known as stochastic gray-box modeling). They emerge...

  8. Modeling stochasticity in biochemical reaction networks

    International Nuclear Information System (INIS)

    Constantino, P H; Vlysidis, M; Smadbeck, P; Kaznessis, Y N

    2016-01-01

    Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts. (topical review)

  9. A study of Monte Carlo methods for weak approximations of stochastic particle systems in the mean-field?

    KAUST Repository

    Haji Ali, Abdul Lateef

    2016-01-08

    I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs). Moreover, this stochastic particle system converges to a stochastic mean-field limit as the number of particles tends to infinity. I start by recalling the results of applying different versions of Multilevel Monte Carlo (MLMC) for particle systems, both with respect to time steps and the number of particles and using a partitioning estimator. Next, I expand on these results by proposing the use of our recent Multi-index Monte Carlo method to obtain improved convergence rates.

  10. A study of Monte Carlo methods for weak approximations of stochastic particle systems in the mean-field?

    KAUST Repository

    Haji Ali, Abdul Lateef

    2016-01-01

    I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs). Moreover, this stochastic particle system converges to a stochastic mean-field limit as the number of particles tends to infinity. I start by recalling the results of applying different versions of Multilevel Monte Carlo (MLMC) for particle systems, both with respect to time steps and the number of particles and using a partitioning estimator. Next, I expand on these results by proposing the use of our recent Multi-index Monte Carlo method to obtain improved convergence rates.

  11. Distributed parallel computing in stochastic modeling of groundwater systems.

    Science.gov (United States)

    Dong, Yanhui; Li, Guomin; Xu, Haizhen

    2013-03-01

    Stochastic modeling is a rapidly evolving, popular approach to the study of the uncertainty and heterogeneity of groundwater systems. However, the use of Monte Carlo-type simulations to solve practical groundwater problems often encounters computational bottlenecks that hinder the acquisition of meaningful results. To improve the computational efficiency, a system that combines stochastic model generation with MODFLOW-related programs and distributed parallel processing is investigated. The distributed computing framework, called the Java Parallel Processing Framework, is integrated into the system to allow the batch processing of stochastic models in distributed and parallel systems. As an example, the system is applied to the stochastic delineation of well capture zones in the Pinggu Basin in Beijing. Through the use of 50 processing threads on a cluster with 10 multicore nodes, the execution times of 500 realizations are reduced to 3% compared with those of a serial execution. Through this application, the system demonstrates its potential in solving difficult computational problems in practical stochastic modeling. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.

  12. Multi-GPU hybrid programming accelerated three-dimensional phase-field model in binary alloy

    Directory of Open Access Journals (Sweden)

    Changsheng Zhu

    2018-03-01

    Full Text Available In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used.

  13. Analytical study on the criticality of the stochastic optimal velocity model

    International Nuclear Information System (INIS)

    Kanai, Masahiro; Nishinari, Katsuhiro; Tokihiro, Tetsuji

    2006-01-01

    In recent works, we have proposed a stochastic cellular automaton model of traffic flow connecting two exactly solvable stochastic processes, i.e., the asymmetric simple exclusion process and the zero range process, with an additional parameter. It is also regarded as an extended version of the optimal velocity model, and moreover it shows particularly notable properties. In this paper, we report that when taking optimal velocity function to be a step function, all of the flux-density graph (i.e. the fundamental diagram) can be estimated. We first find that the fundamental diagram consists of two line segments resembling an inversed-λ form, and next identify their end-points from a microscopic behaviour of vehicles. It is notable that by using a microscopic parameter which indicates a driver's sensitivity to the traffic situation, we give an explicit formula for the critical point at which a traffic jam phase arises. We also compare these analytical results with those of the optimal velocity model, and point out the crucial differences between them

  14. STOCHASTIC CHARACTERISTICS AND MODELING OF RELATIVE ...

    African Journals Online (AJOL)

    Test

    Results are highly accurate and promising for all models based on Lewis' criteria. ... hydrological cycle. Future increases in ... STOCHASTIC CHARACTERISTICS AND MODELING OF RELATIVE HUMIDITY OF OGUN BASIN, NIGERIA. 71 ...

  15. Effects of Nozzle Diameter on Diesel Spray Flames: A numerical study using an Eulerian Stochastic Field Method

    DEFF Research Database (Denmark)

    Pang, Kar Mun; Jangi, Mehdi; Bai, Xue-Song

    2017-01-01

    The present numerical study aims to assess the performance of an Eulerian Stochastic Field (ESF) model in simulating spray flames produced by three fuel injectors with different nozzle diameters of 100 μm, 180 μm and 363 μm. A comparison to the measurements shows that although the simulated ignit...... serve as an important tool for the simulation of spray flames in marine diesel engines, where fuel injectors with different nozzle diameters are applied for pilot and main injections.......The present numerical study aims to assess the performance of an Eulerian Stochastic Field (ESF) model in simulating spray flames produced by three fuel injectors with different nozzle diameters of 100 μm, 180 μm and 363 μm. A comparison to the measurements shows that although the simulated...... ignition delay times are consistently overestimated, the relative differences remain below 28%. Furthermore, the change of the averaged pressure rise with respect to the variation of nozzle diameter is captured by the model. The simulated flame lift-off lengths also agree with the measurements...

  16. Deterministic and stochastic CTMC models from Zika disease transmission

    Science.gov (United States)

    Zevika, Mona; Soewono, Edy

    2018-03-01

    Zika infection is one of the most important mosquito-borne diseases in the world. Zika virus (ZIKV) is transmitted by many Aedes-type mosquitoes including Aedes aegypti. Pregnant women with the Zika virus are at risk of having a fetus or infant with a congenital defect and suffering from microcephaly. Here, we formulate a Zika disease transmission model using two approaches, a deterministic model and a continuous-time Markov chain stochastic model. The basic reproduction ratio is constructed from a deterministic model. Meanwhile, the CTMC stochastic model yields an estimate of the probability of extinction and outbreaks of Zika disease. Dynamical simulations and analysis of the disease transmission are shown for the deterministic and stochastic models.

  17. Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

    Directory of Open Access Journals (Sweden)

    Zoran Stanković

    2015-01-01

    Full Text Available An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN and the Multilayer Perceptron (MLP networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA estimation within the training boundaries which is illustrated on the appropriate example.

  18. Weather Derivatives and Stochastic Modelling of Temperature

    Directory of Open Access Journals (Sweden)

    Fred Espen Benth

    2011-01-01

    Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

  19. Stochastic Modeling Of Wind Turbine Drivetrain Components

    DEFF Research Database (Denmark)

    Rafsanjani, Hesam Mirzaei; Sørensen, John Dalsgaard

    2014-01-01

    reliable components are needed for wind turbine. In this paper focus is on reliability of critical components in drivetrain such as bearings and shafts. High failure rates of these components imply a need for more reliable components. To estimate the reliability of these components, stochastic models...... are needed for initial defects and damage accumulation. In this paper, stochastic models are formulated considering some of the failure modes observed in these components. The models are based on theoretical considerations, manufacturing uncertainties, size effects of different scales. It is illustrated how...

  20. Applied stochastic modelling

    CERN Document Server

    Morgan, Byron JT; Tanner, Martin Abba; Carlin, Bradley P

    2008-01-01

    Introduction and Examples Introduction Examples of data sets Basic Model Fitting Introduction Maximum-likelihood estimation for a geometric model Maximum-likelihood for the beta-geometric model Modelling polyspermy Which model? What is a model for? Mechanistic models Function Optimisation Introduction MATLAB: graphs and finite differences Deterministic search methods Stochastic search methods Accuracy and a hybrid approach Basic Likelihood ToolsIntroduction Estimating standard errors and correlations Looking at surfaces: profile log-likelihoods Confidence regions from profiles Hypothesis testing in model selectionScore and Wald tests Classical goodness of fit Model selection biasGeneral Principles Introduction Parameterisation Parameter redundancy Boundary estimates Regression and influence The EM algorithm Alternative methods of model fitting Non-regular problemsSimulation Techniques Introduction Simulating random variables Integral estimation Verification Monte Carlo inference Estimating sampling distributi...

  1. On changes of measure in stochastic volatility models

    Directory of Open Access Journals (Sweden)

    Bernard Wong

    2006-01-01

    models. This had led many researchers to “assume the condition away,” even though the condition is not innocuous, and nonsensical results can occur if it is in fact not satisfied. We provide an applicable theorem to check the conditions for a general class of Markovian stochastic volatility models. As an example we will also provide a detailed analysis of the Stein and Stein and Heston stochastic volatility models.

  2. Gompertzian stochastic model with delay effect to cervical cancer growth

    International Nuclear Information System (INIS)

    Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah

    2015-01-01

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits

  3. Gompertzian stochastic model with delay effect to cervical cancer growth

    Energy Technology Data Exchange (ETDEWEB)

    Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2015-02-03

    In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.

  4. Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process

    Science.gov (United States)

    Turner, Douglas C.; Ladde, Gangaram S.

    2018-03-01

    Analytical solutions, discretization schemes and simulation results are presented for the time delay deterministic differential equation model of the solar dynamo presented by Wilmot-Smith et al. In addition, this model is extended under stochastic Gaussian white noise parametric fluctuations. The introduction of stochastic fluctuations incorporates variables affecting the dynamo process in the solar interior, estimation error of parameters, and uncertainty of the α-effect mechanism. Simulation results are presented and analyzed to exhibit the effects of stochastic parametric volatility-dependent perturbations. The results generalize and extend the work of Hazra et al. In fact, some of these results exhibit the oscillatory dynamic behavior generated by the stochastic parametric additative perturbations in the absence of time delay. In addition, the simulation results of the modified stochastic models influence the change in behavior of the very recently developed stochastic model of Hazra et al.

  5. A stochastic model simulating the capture of pathogenic micro-organisms by superparamagnetic particles in an isodynamic magnetic field

    International Nuclear Information System (INIS)

    Rotariu, O; Strachan, N J C; Badescu, V

    2004-01-01

    The method of immunomagnetic separation (IMS) has become an established technique to concentrate and separate animal cells, biologically active compounds and pathogenic micro-organisms from clinical, food and environmental matrices. One drawback of this technique is that the analysis is only possible for small sample volumes. We have developed a stochastic model that involves numerical simulations to optimize the process of concentration of pathogenic micro-organisms onto superparamagnetic carrier particles (SCPs) in a gradient magnetic field. Within the range of the system parameters varied in the simulations, optimal conditions favour larger particles with higher magnetite concentrations. The dependence on magnetic field intensity and gradient together with concentration of particles and micro-organisms was found to be less important for larger SCPs but these parameters can influence the values of the collision time for small particles. These results will be useful in aiding the design of apparatus for immunomagnetic separation from large volume samples

  6. Stochastic models of the Social Security trust funds.

    Science.gov (United States)

    Burdick, Clark; Manchester, Joyce

    Each year in March, the Board of Trustees of the Social Security trust funds reports on the current and projected financial condition of the Social Security programs. Those programs, which pay monthly benefits to retired workers and their families, to the survivors of deceased workers, and to disabled workers and their families, are financed through the Old-Age, Survivors, and Disability Insurance (OASDI) Trust Funds. In their 2003 report, the Trustees present, for the first time, results from a stochastic model of the combined OASDI trust funds. Stochastic modeling is an important new tool for Social Security policy analysis and offers the promise of valuable new insights into the financial status of the OASDI trust funds and the effects of policy changes. The results presented in this article demonstrate that several stochastic models deliver broadly consistent results even though they use very different approaches and assumptions. However, they also show that the variation in trust fund outcomes differs as the approach and assumptions are varied. Which approach and assumptions are best suited for Social Security policy analysis remains an open question. Further research is needed before the promise of stochastic modeling is fully realized. For example, neither parameter uncertainty nor variability in ultimate assumption values is recognized explicitly in the analyses. Despite this caveat, stochastic modeling results are already shedding new light on the range and distribution of trust fund outcomes that might occur in the future.

  7. Fixation of Cs to marine sediments estimated by a stochastic modelling approach.

    Science.gov (United States)

    Børretzen, Peer; Salbu, Brit

    2002-01-01

    Dumping of nuclear waste in the Kara Sea represents a potential source of radioactive contamination to the Arctic Seas in the future. The mobility of 137Cs ions leached from the waste will depend on the interactions with sediment particles. Whether sediments will act as a continuous permanent sink for released 137Cs, or contaminated sediments will serve as a diffuse source of 137Cs in the future, depends on the interaction kinetics and binding mechanisms involved. The main purpose of this paper is to study the performance of different stochastic models using kinetic information to estimate the time needed for Cs ions to become irreversibly fixed within the sediments. The kinetic information was obtained from 134Cs tracer sorption and desorption (sequential extractions) experiments, conducted over time, using sediments from the Stepovogo Fjord waste dumping site, on the east coast of Novaya Zemlya. Results show that 134Cs ions interact rapidly with the surfaces of the Stepovogo sediment, with an estimated distribution coefficient Kd(eq) of 300 ml/g (or 13m2/g), and the 134Cs ions are increasingly irreversibly fixed to the sediment over time. For the first time, stochastic theory has been utilised for sediment-seawater systems to estimate the mean residence times (MRTs) of Cs ions in operationally defined sediment phases described by compartment models. In the present work, two different stochastic models (i) a Markov process model (MP) being analogous to deterministic compartment models, and (ii) a semi-Markov process model (SMP) which should be physically more relevant for inhomogeneous systems, have been compared. As similar results were obtained using the two models, the less complicated MP model was utilised to predict the time needed for an average Cs ion to become irreversibly fixed in the Stepovogo sediments. According the model, approximately 1100 days of contact time between Cs ions and sediments is needed before 50% of the 134Cs ion becomes fixed in the

  8. A Stochastic Maximum Principle for General Mean-Field Systems

    International Nuclear Information System (INIS)

    Buckdahn, Rainer; Li, Juan; Ma, Jin

    2016-01-01

    In this paper we study the optimal control problem for a class of general mean-field stochastic differential equations, in which the coefficients depend, nonlinearly, on both the state process as well as of its law. In particular, we assume that the control set is a general open set that is not necessary convex, and the coefficients are only continuous on the control variable without any further regularity or convexity. We validate the approach of Peng (SIAM J Control Optim 2(4):966–979, 1990) by considering the second order variational equations and the corresponding second order adjoint process in this setting, and we extend the Stochastic Maximum Principle of Buckdahn et al. (Appl Math Optim 64(2):197–216, 2011) to this general case.

  9. A Stochastic Maximum Principle for General Mean-Field Systems

    Energy Technology Data Exchange (ETDEWEB)

    Buckdahn, Rainer, E-mail: Rainer.Buckdahn@univ-brest.fr [Université de Bretagne-Occidentale, Département de Mathématiques (France); Li, Juan, E-mail: juanli@sdu.edu.cn [Shandong University, Weihai, School of Mathematics and Statistics (China); Ma, Jin, E-mail: jinma@usc.edu [University of Southern California, Department of Mathematics (United States)

    2016-12-15

    In this paper we study the optimal control problem for a class of general mean-field stochastic differential equations, in which the coefficients depend, nonlinearly, on both the state process as well as of its law. In particular, we assume that the control set is a general open set that is not necessary convex, and the coefficients are only continuous on the control variable without any further regularity or convexity. We validate the approach of Peng (SIAM J Control Optim 2(4):966–979, 1990) by considering the second order variational equations and the corresponding second order adjoint process in this setting, and we extend the Stochastic Maximum Principle of Buckdahn et al. (Appl Math Optim 64(2):197–216, 2011) to this general case.

  10. Stochastic layer scaling in the two-wire model for divertor tokamaks

    Science.gov (United States)

    Ali, Halima; Punjabi, Alkesh; Boozer, Allen

    2009-06-01

    The question of magnetic field structure in the vicinity of the separatrix in divertor tokamaks is studied. The authors have investigated this problem earlier in a series of papers, using various mathematical techniques. In the present paper, the two-wire model (TWM) [Reiman, A. 1996 Phys. Plasmas 3, 906] is considered. It is noted that, in the TWM, it is useful to consider an extra equation expressing magnetic flux conservation. This equation does not add any more information to the TWM, since the equation is derived from the TWM. This equation is useful for controlling the step size in the numerical integration of the TWM equations. The TWM with the extra equation is called the flux-preserving TWM. Nevertheless, the technique is apparently still plagued by numerical inaccuracies when the perturbation level is low, resulting in an incorrect scaling of the stochastic layer width. The stochastic broadening of the separatrix in the flux-preserving TWM is compared with that in the low mn (poloidal mode number m and toroidal mode number n) map (LMN) [Ali, H., Punjabi, A., Boozer, A. and Evans, T. 2004 Phys. Plasmas 11, 1908]. The flux-preserving TWM and LMN both give Boozer-Rechester 0.5 power scaling of the stochastic layer width with the amplitude of magnetic perturbation when the perturbation is sufficiently large [Boozer, A. and Rechester, A. 1978, Phys. Fluids 21, 682]. The flux-preserving TWM gives a larger stochastic layer width when the perturbation is low, while the LMN gives correct scaling in the low perturbation region. Area-preserving maps such as the LMN respect the Hamiltonian structure of field line trajectories, and have the added advantage of computational efficiency. Also, for a $1\\frac12$ degree of freedom Hamiltonian system such as field lines, maps do not give Arnold diffusion.

  11. Biochemical Network Stochastic Simulator (BioNetS: software for stochastic modeling of biochemical networks

    Directory of Open Access Journals (Sweden)

    Elston Timothy C

    2004-03-01

    Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

  12. Electricity Market Stochastic Dynamic Model and Its Mean Stability Analysis

    Directory of Open Access Journals (Sweden)

    Zhanhui Lu

    2014-01-01

    Full Text Available Based on the deterministic dynamic model of electricity market proposed by Alvarado, a stochastic electricity market model, considering the random nature of demand sides, is presented in this paper on the assumption that generator cost function and consumer utility function are quadratic functions. The stochastic electricity market model is a generalization of the deterministic dynamic model. Using the theory of stochastic differential equations, stochastic process theory, and eigenvalue techniques, the determining conditions of the mean stability for this electricity market model under small Gauss type random excitation are provided and testified theoretically. That is, if the demand elasticity of suppliers is nonnegative and the demand elasticity of consumers is negative, then the stochastic electricity market model is mean stable. It implies that the stability can be judged directly by initial data without any computation. Taking deterministic electricity market data combined with small Gauss type random excitation as numerical samples to interpret random phenomena from a statistical perspective, the results indicate the conclusions above are correct, valid, and practical.

  13. Phase field modeling of dendritic coarsening during isothermal

    Directory of Open Access Journals (Sweden)

    Zhang Yutuo

    2011-08-01

    Full Text Available Dendritic coarsening in Al-2mol%Si alloy during isothermal solidification at 880K was investigated by phase field modeling. Three coarsening mechanisms operate in the alloy: (a melting of small dendrite arms; (b coalescence of dendrites near the tips leading to the entrapment of liquid droplets; (c smoothing of dendrites. Dendrite melting is found to be dominant in the stage of dendritic growth, whereas coalescence of dendrites and smoothing of dendrites are dominant during isothermal holding. The simulated results provide a better understanding of dendrite coarsening during isothermal solidification.

  14. Aspects if stochastic models for short-term hydropower scheduling and bidding

    Energy Technology Data Exchange (ETDEWEB)

    Belsnes, Michael Martin [Sintef Energy, Trondheim (Norway); Follestad, Turid [Sintef Energy, Trondheim (Norway); Wolfgang, Ove [Sintef Energy, Trondheim (Norway); Fosso, Olav B. [Dep. of electric power engineering NTNU, Trondheim (Norway)

    2012-07-01

    This report discusses challenges met when turning from deterministic to stochastic decision support models for short-term hydropower scheduling and bidding. The report describes characteristics of the short-term scheduling and bidding problem, different market and bidding strategies, and how a stochastic optimization model can be formulated. A review of approaches for stochastic short-term modelling and stochastic modelling for the input variables inflow and market prices is given. The report discusses methods for approximating the predictive distribution of uncertain variables by scenario trees. Benefits of using a stochastic over a deterministic model are illustrated by a case study, where increased profit is obtained to a varying degree depending on the reservoir filling and price structure. Finally, an approach for assessing the effect of using a size restricted scenario tree to approximate the predictive distribution for stochastic input variables is described. The report is a summary of the findings of Work package 1 of the research project #Left Double Quotation Mark#Optimal short-term scheduling of wind and hydro resources#Right Double Quotation Mark#. The project aims at developing a prototype for an operational stochastic short-term scheduling model. Based on the investigations summarized in the report, it is concluded that using a deterministic equivalent formulation of the stochastic optimization problem is convenient and sufficient for obtaining a working prototype. (author)

  15. Influence of the mode of deformation on recrystallisation behaviour of titanium through experiments, mean field theory and phase field model

    Science.gov (United States)

    Athreya, C. N.; Mukilventhan, A.; Suwas, Satyam; Vedantam, Srikanth; Subramanya Sarma, V.

    2018-04-01

    The influence of the mode of deformation on recrystallisation behaviour of Ti was studied by experiments and modelling. Ti samples were deformed through torsion and rolling to the same equivalent strain of 0.5. The deformed samples were annealed at different temperatures for different time durations and the recrystallisation kinetics were compared. Recrystallisation is found to be faster in the rolled samples compared to the torsion deformed samples. This is attributed to the differences in stored energy and number of nuclei per unit area in the two modes of deformation. Considering decay in stored energy during recrystallisation, the grain boundary mobility was estimated through a mean field model. The activation energy for recrystallisation obtained from experiments matched with the activation energy for grain boundary migration obtained from mobility calculation. A multi-phase field model (with mobility estimated from the mean field model as a constitutive input) was used to simulate the kinetics, microstructure and texture evolution. The recrystallisation kinetics and grain size distributions obtained from experiments matched reasonably well with the phase field simulations. The recrystallisation texture predicted through phase field simulations compares well with experiments though few additional texture components are present in simulations. This is attributed to the anisotropy in grain boundary mobility, which is not accounted for in the present study.

  16. Characterizing economic trends by Bayesian stochastic model specifi cation search

    OpenAIRE

    Grassi, Stefano; Proietti, Tommaso

    2010-01-01

    We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends. In particular, we formulate autoregressive models with stochastic trends components and decide on whether a specific feature of the series, i.e. the underlying level and/or the rate...

  17. Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates

    NARCIS (Netherlands)

    Jiang, G.J.; van der Sluis, P.J.

    2000-01-01

    This paper specifies a multivariate stochastic volatility (SV) model for the S&P500 index and spot interest rate processes. We first estimate the multivariate SV model via the efficient method of moments (EMM) technique based on observations of underlying state variables, and then investigate the

  18. Dynamic optimization deterministic and stochastic models

    CERN Document Server

    Hinderer, Karl; Stieglitz, Michael

    2016-01-01

    This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

  19. Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification

    Science.gov (United States)

    Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.

    2017-12-01

    Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data

  20. Stochastic models for atmospheric dispersion

    DEFF Research Database (Denmark)

    Ditlevsen, Ove Dalager

    2003-01-01

    Simple stochastic differential equation models have been applied by several researchers to describe the dispersion of tracer particles in the planetary atmospheric boundary layer and to form the basis for computer simulations of particle paths. To obtain the drift coefficient, empirical vertical...... positions close to the boundaries. Different rules have been suggested in the literature with justifications based on simulation studies. Herein the relevant stochastic differential equation model is formulated in a particular way. The formulation is based on the marginal transformation of the position...... velocity distributions that depend on height above the ground both with respect to standard deviation and skewness are substituted into the stationary Fokker/Planck equation. The particle position distribution is taken to be uniform *the well/mixed condition( and also a given dispersion coefficient...

  1. Hopf bifurcation of the stochastic model on business cycle

    International Nuclear Information System (INIS)

    Xu, J; Wang, H; Ge, G

    2008-01-01

    A stochastic model on business cycle was presented in thas paper. Simplifying the model through the quasi Hamiltonian theory, the Ito diffusion process was obtained. According to Oseledec multiplicative ergodic theory and singular boundary theory, the conditions of local and global stability were acquired. Solving the stationary FPK equation and analyzing the stationary probability density, the stochastic Hopf bifurcation was explained. The result indicated that the change of parameter awas the key factor to the appearance of the stochastic Hopf bifurcation

  2. Models of gas-grain chemistry in interstellar cloud cores with a stochastic approach to surface chemistry

    Science.gov (United States)

    Stantcheva, T.; Herbst, E.

    2004-08-01

    We present a gas-grain model of homogeneous cold cloud cores with time-independent physical conditions. In the model, the gas-phase chemistry is treated via rate equations while the diffusive granular chemistry is treated stochastically. The two phases are coupled through accretion and evaporation. A small network of surface reactions accounts for the surface production of the stable molecules water, formaldehyde, methanol, carbon dioxide, ammonia, and methane. The calculations are run for a time of 107 years at three different temperatures: 10 K, 15 K, and 20 K. The results are compared with those produced in a totally deterministic gas-grain model that utilizes the rate equation method for both the gas-phase and surface chemistry. The results of the different models are in agreement for the abundances of the gaseous species except for later times when the surface chemistry begins to affect the gas. The agreement for the surface species, however, is somewhat mixed. The average abundances of highly reactive surface species can be orders of magnitude larger in the stochastic-deterministic model than in the purely deterministic one. For non-reactive species, the results of the models can disagree strongly at early times, but agree to well within an order of magnitude at later times for most molecules. Strong exceptions occur for CO and H2CO at 10 K, and for CO2 at 20 K. The agreement seems to be best at a temperature of 15 K. As opposed to the use of the normal rate equation method of surface chemistry, the modified rate method is in significantly better agreement with the stochastic-deterministic approach. Comparison with observations of molecular ices in dense clouds shows mixed agreement.

  3. Plasma transport in the stochastic fields at the tokamak edge. Final report, February 15, 1993--February 14, 1994

    International Nuclear Information System (INIS)

    Punjabi, A.

    1994-01-01

    The purpose of this project is to calculate the contribution of field line diffusion to particle diffusion in the stochastic magnetic field at the tokamak edge. The author uses the approach of quasi magnetic surfaces. If the magnetic field line makes sufficiently large number of toroidal transits before suffering large radial excursion, then the method of quasi magnetic surface is valid for this problem. This method has three components: determination of particle drift trajectories, a model for magnetic field configuration, and determination of field line diffusion

  4. Stochastic models for predicting pitting corrosion damage of HLRW containers

    International Nuclear Information System (INIS)

    Henshall, G.A.

    1991-10-01

    Stochastic models for predicting aqueous pitting corrosion damage of high-level radioactive-waste containers are described. These models could be used to predict the time required for the first pit to penetrate a container and the increase in the number of breaches at later times, both of which would be useful in the repository system performance analysis. Monte Carlo implementations of the stochastic models are described, and predictions of induction time, survival probability and pit depth distributions are presented. These results suggest that the pit nucleation probability decreases with exposure time and that pit growth may be a stochastic process. The advantages and disadvantages of the stochastic approach, methods for modeling the effects of environment, and plans for future work are discussed

  5. Discussions on the non-equilibrium effects in the quantitative phase field model of binary alloys

    International Nuclear Information System (INIS)

    Zhi-Jun, Wang; Jin-Cheng, Wang; Gen-Cang, Yang

    2010-01-01

    All the quantitative phase field models try to get rid of the artificial factors of solutal drag, interface diffusion and interface stretch in the diffuse interface. These artificial non-equilibrium effects due to the introducing of diffuse interface are analysed based on the thermodynamic status across the diffuse interface in the quantitative phase field model of binary alloys. Results indicate that the non-equilibrium effects are related to the negative driving force in the local region of solid side across the diffuse interface. The negative driving force results from the fact that the phase field model is derived from equilibrium condition but used to simulate the non-equilibrium solidification process. The interface thickness dependence of the non-equilibrium effects and its restriction on the large scale simulation are also discussed. (cross-disciplinary physics and related areas of science and technology)

  6. High-performance phase-field modeling

    KAUST Repository

    Vignal, Philippe; Sarmiento, Adel; Cortes, Adriano Mauricio; Dalcin, L.; Collier, N.; Calo, Victor M.

    2015-01-01

    and phase-field crystal equation will be presented, which corroborate the theoretical findings, and illustrate the robustness of the method. Results related to more challenging examples, namely the Navier-Stokes Cahn-Hilliard and a diusion-reaction Cahn-Hilliard system, will also be presented. The implementation was done in PetIGA and PetIGA-MF, high-performance Isogeometric Analysis frameworks [1, 3], designed to handle non-linear, time-dependent problems.

  7. Brain-inspired Stochastic Models and Implementations

    KAUST Repository

    Al-Shedivat, Maruan

    2015-05-12

    One of the approaches to building artificial intelligence (AI) is to decipher the princi- ples of the brain function and to employ similar mechanisms for solving cognitive tasks, such as visual perception or natural language understanding, using machines. The recent breakthrough, named deep learning, demonstrated that large multi-layer networks of arti- ficial neural-like computing units attain remarkable performance on some of these tasks. Nevertheless, such artificial networks remain to be very loosely inspired by the brain, which rich structures and mechanisms may further suggest new algorithms or even new paradigms of computation. In this thesis, we explore brain-inspired probabilistic mechanisms, such as neural and synaptic stochasticity, in the context of generative models. The two questions we ask here are: (i) what kind of models can describe a neural learning system built of stochastic components? and (ii) how can we implement such systems e ̆ciently? To give specific answers, we consider two well known models and the corresponding neural architectures: the Naive Bayes model implemented with a winner-take-all spiking neural network and the Boltzmann machine implemented in a spiking or non-spiking fashion. We propose and analyze an e ̆cient neuromorphic implementation of the stochastic neu- ral firing mechanism and study the e ̄ects of synaptic unreliability on learning generative energy-based models implemented with neural networks.

  8. Simulation of the stochastic wave loads using a physical modeling approach

    DEFF Research Database (Denmark)

    Liu, W.F.; Sichani, Mahdi Teimouri; Nielsen, Søren R.K.

    2013-01-01

    In analyzing stochastic dynamic systems, analysis of the system uncertainty due to randomness in the loads plays a crucial role. Typically time series of the stochastic loads are simulated using traditional random phase method. This approach combined with fast Fourier transform algorithm makes...... reliability or its uncertainty. Moreover applicability of the probability density evolution method on engineering problems faces critical difficulties when the system embeds too many random variables. Hence it is useful to devise a method which can make realization of the stochastic load processes with low...

  9. Stochastic models to simulate paratuberculosis in dairy herds

    DEFF Research Database (Denmark)

    Nielsen, Søren Saxmose; Weber, M.F.; Kudahl, Anne Margrethe Braad

    2011-01-01

    Stochastic simulation models are widely accepted as a means of assessing the impact of changes in daily management and the control of different diseases, such as paratuberculosis, in dairy herds. This paper summarises and discusses the assumptions of four stochastic simulation models and their use...... the models are somewhat different in their underlying principles and do put slightly different values on the different strategies, their overall findings are similar. Therefore, simulation models may be useful in planning paratuberculosis strategies in dairy herds, although as with all models caution...

  10. Linking agent-based models and stochastic models of financial markets.

    Science.gov (United States)

    Feng, Ling; Li, Baowen; Podobnik, Boris; Preis, Tobias; Stanley, H Eugene

    2012-05-29

    It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.

  11. Stochastic differential equation model to Prendiville processes

    International Nuclear Information System (INIS)

    Granita; Bahar, Arifah

    2015-01-01

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution

  12. Stochastic differential equation model to Prendiville processes

    Energy Technology Data Exchange (ETDEWEB)

    Granita, E-mail: granitafc@gmail.com [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); Bahar, Arifah [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); UTM Center for Industrial & Applied Mathematics (UTM-CIAM) (Malaysia)

    2015-10-22

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution.

  13. Stochastic properties of the Friedman dynamical system

    International Nuclear Information System (INIS)

    Szydlowski, M.; Heller, M.; Golda, Z.

    1985-01-01

    Some mathematical aspects of the stochastic cosmology are discussed in the corresponding ordinary Friedman world models. In particulare, it is shown that if the strong and Lorentz energy conditions are known, or the potential function is given, or a stochastic measure is suitably defined then the structure of the phase plane of the Friedman dynamical system is determined. 11 refs., 2 figs. (author)

  14. Stochastic growth logistic model with aftereffect for batch fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Rosli, Norhayati; Ayoubi, Tawfiqullah [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah; Rahman, Haliza Abdul [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia); Salleh, Madihah Md [Department of Biotechnology Industry, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2014-06-19

    In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

  15. Stochastic growth logistic model with aftereffect for batch fermentation process

    Science.gov (United States)

    Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

    2014-06-01

    In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

  16. Stochastic growth logistic model with aftereffect for batch fermentation process

    International Nuclear Information System (INIS)

    Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

    2014-01-01

    In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits

  17. Impact of stochastic primordial magnetic fields on the scalar contribution to cosmic microwave background anisotropies

    International Nuclear Information System (INIS)

    Finelli, Fabio; Paci, Francesco; Paoletti, Daniela

    2008-01-01

    We study the impact of a stochastic background of primordial magnetic fields on the scalar contribution of cosmic microwave background (CMB) anisotropies and on the matter power spectrum. We give the correct initial conditions for cosmological perturbations and the exact expressions for the energy density and Lorentz force associated to the stochastic background of primordial magnetic fields, given a power-law for their spectra cut at a damping scale. The dependence of the CMB temperature and polarization spectra on the relevant parameters of the primordial magnetic fields is illustrated.

  18. Stochastic models of intracellular transport

    KAUST Repository

    Bressloff, Paul C.

    2013-01-09

    The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an overdamped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of adenosine triphosphate hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review a wide range of analytical methods and models of intracellular transport is presented. In the case of diffusive transport, narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion are considered. In the case of active transport, Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasi-steady-state reduction methods, and mean-field approximations are considered. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures. © 2013 American Physical Society.

  19. Towards Model Checking Stochastic Process Algebra

    NARCIS (Netherlands)

    Hermanns, H.; Grieskamp, W.; Santen, T.; Katoen, Joost P.; Stoddart, B.; Meyer-Kayser, J.; Siegle, M.

    2000-01-01

    Stochastic process algebras have been proven useful because they allow behaviour-oriented performance and reliability modelling. As opposed to traditional performance modelling techniques, the behaviour- oriented style supports composition and abstraction in a natural way. However, analysis of

  20. Lateral diffusion of the topological charge density in stochastic optical fields

    CSIR Research Space (South Africa)

    Roux, FS

    2010-01-01

    Full Text Available Stochastic (i.e. random and quasi-random) optical fields may contain distributions of optical vortices that are represented by non-uniform topological charge densities. Numerical simulations are used to investigate the evolution under free...

  1. Stochastic forward and inverse groundwater flow and solute transport modeling

    NARCIS (Netherlands)

    Janssen, G.M.C.M.

    2008-01-01

    Keywords: calibration, inverse modeling, stochastic modeling, nonlinear biodegradation, stochastic-convective, advective-dispersive, travel time, network design, non-Gaussian distribution, multimodal distribution, representers

    This thesis offers three new approaches that contribute

  2. Phase-space quantization of field theory

    International Nuclear Information System (INIS)

    Curtright, T.; Zachos, C.

    1999-01-01

    In this lecture, a limited introduction of gauge invariance in phase-space is provided, predicated on canonical transformations in quantum phase-space. Exact characteristic trajectories are also specified for the time-propagating Wigner phase-space distribution function: they are especially simple--indeed, classical--for the quantized simple harmonic oscillator. This serves as the underpinning of the field theoretic Wigner functional formulation introduced. Scalar field theory is thus reformulated in terms of distributions in field phase-space. This is a pedagogical selection from work published and reported at the Yukawa Institute Workshop ''Gauge Theory and Integrable Models'', 26-29 January, 1999

  3. Stochastic models for predicting environmental impact in aquatic ecosystems

    International Nuclear Information System (INIS)

    Stewart-Oaten, A.

    1986-01-01

    The purpose of stochastic predictions are discussed in relation to the environmental impacts of nuclear power plants on aquatic ecosystems. One purpose is to aid in making rational decisions about whether a power plant should be built, where, and how it should be designed. The other purpose is to check on the models themselves in the light of what eventually happens. The author discusses the role or statistical decision theory in the decision-making problem. Various types of stochastic models and their problems are presented. In addition some suggestions are made for generating usable stochastic models, and checking and improving on them. 12 references

  4. Phase-field simulation of nucleation and growth of M{sub 23}C{sub 6} carbide and ferromagnetic phases during creep deformation in Type 304 steel

    Energy Technology Data Exchange (ETDEWEB)

    Tsukada, Yuhki, E-mail: tsukada@silky.numse.nagoya-u.ac.j [Department of Materials, Physics and Energy Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan); Shiraki, Atsuhiro; Murata, Yoshinori [Department of Materials, Physics and Energy Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan); Takaya, Shigeru [Japan Atomic Energy Agency, 4002 Narita-cho, O-arai-machi, Higashi-ibaraki-gun, Ibaraki 311-1393 (Japan); Koyama, Toshiyuki [National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 (Japan); Morinaga, Masahiko [Department of Materials, Physics and Energy Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603 (Japan)

    2010-06-15

    A phase-field method was applied to the simulation of simultaneous nucleation and growth of both M{sub 23}C{sub 6} carbide and ferromagnetic {alpha} phases during the creep process in Type 304 steel. Nucleation events of these product phases were explicitly introduced through a probabilistic Poisson seeding process based on local nucleation rates that were calculated as a function of local concentration. The defect energy of the creep dislocations near the carbides, which increases during creep, was integrated into the nucleation driving force for the {alpha} phase. The simulation used in this study accurately reproduced changes in the amounts of the precipitated phases as a function of creep time. Furthermore, we examine the effect of the dislocation density on precipitation of the {alpha} phase, and show that the phase-field method is useful for examining the stochastic and kinetic phenomenon of phase transformation.

  5. A simple stochastic model for dipole moment fluctuations in numerical dynamo simulations

    Directory of Open Access Journals (Sweden)

    Domenico G. eMeduri

    2016-04-01

    Full Text Available Earth's axial dipole field changes in a complex fashion on many differenttime scales ranging from less than a year to tens of million years.Documenting, analysing, and replicating this intricate signalis a challenge for data acquisition, theoretical interpretation,and dynamo modelling alike. Here we explore whether axial dipole variationscan be described by the superposition of a slow deterministic driftand fast stochastic fluctuations, i.e. by a Langevin-type system.The drift term describes the time averaged behaviour of the axial dipole variations,whereas the stochastic part mimics complex flow interactions over convective time scales.The statistical behaviour of the system is described by a Fokker-Planck equation whichallows useful predictions, including the average rates of dipole reversals and excursions.We analyse several numerical dynamo simulations, most of which havebeen integrated particularly long in time, and also the palaeomagneticmodel PADM2M which covers the past 2 Myr.The results show that the Langevin description provides a viable statistical modelof the axial dipole variations on time scales longer than about 1 kyr.For example, the axial dipole probability distribution and the average reversalrate are successfully predicted.The exception is PADM2M where the stochastic model reversal rate seems too low.The dependence of the drift on the axial dipolemoment reveals the nonlinear interactions that establish thedynamo balance. A separate analysis of inductive and diffusive magnetic effectsin three dynamo simulations suggests that the classical quadraticquenching of induction predicted by mean-field theory seems at work.

  6. Introduction to modeling and analysis of stochastic systems

    CERN Document Server

    Kulkarni, V G

    2011-01-01

    This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems. The book is devoted to the study of important classes of stochastic processes: discrete and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematically studies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples, and case studies. The book provides a concise review of probability in the appendix. The book emphasizes numerical answers to the problems. A collection of MATLAB programs to accompany...

  7. Employment, Production and Consumption model: Patterns of phase transitions

    Science.gov (United States)

    Lavička, H.; Lin, L.; Novotný, J.

    2010-04-01

    We have simulated the model of Employment, Production and Consumption (EPC) using Monte Carlo. The EPC model is an agent based model that mimics very basic rules of industrial economy. From the perspective of physics, the nature of the interactions in the EPC model represents multi-agent interactions where the relations among agents follow the key laws for circulation of capital and money. Monte Carlo simulations of the stochastic model reveal phase transition in the model economy. The two phases are the phase with full unemployment and the phase with nearly full employment. The economy switches between these two states suddenly as a reaction to a slight variation in the exogenous parameter, thus the system exhibits strong non-linear behavior as a response to the change of the exogenous parameters.

  8. Stochastic Averaging and Stochastic Extremum Seeking

    CERN Document Server

    Liu, Shu-Jun

    2012-01-01

    Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering  and analysis of bacterial  convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...

  9. Diagram of collisional regimes for particle diffusion in a stochastic magnetic field

    International Nuclear Information System (INIS)

    Misguich, J.H.; Balescu, R.

    1995-01-01

    This document deals with static stochastic fields, where magnetic lines experience exponential separation and magnetic diffusion. It more particularly focuses on the diffusivity of collisional particles in such a fields and presents a general graph which describes most regimes of collisional and weakly collisional diffusion for guiding centers in a time-independent magnetic field. (TEC). 9 refs., 1 fig., 2 tabs

  10. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...

  11. Threshold Dynamics of a Stochastic Chemostat Model with Two Nutrients and One Microorganism

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2017-01-01

    Full Text Available A new stochastic chemostat model with two substitutable nutrients and one microorganism is proposed and investigated. Firstly, for the corresponding deterministic model, the threshold for extinction and permanence of the microorganism is obtained by analyzing the stability of the equilibria. Then, for the stochastic model, the threshold of the stochastic chemostat for extinction and permanence of the microorganism is explored. Difference of the threshold of the deterministic model and the stochastic model shows that a large stochastic disturbance can affect the persistence of the microorganism and is harmful to the cultivation of the microorganism. To illustrate this phenomenon, we give some computer simulations with different intensity of stochastic noise disturbance.

  12. Applications of stochastic geometry in image analysis

    NARCIS (Netherlands)

    Lieshout, van M.N.M.; Kendall, W.S.; Molchanov, I.S.

    2009-01-01

    A discussion is given of various stochastic geometry models (random fields, sequential object processes, polygonal field models) which can be used in intermediate and high-level image analysis. Two examples are presented of actual image analysis problems (motion tracking in video,

  13. Sink efficiency calculation of dislocations in irradiated materials by phase-field modelling

    International Nuclear Information System (INIS)

    Rouchette, Adrien

    2015-01-01

    The aim of this work is to develop a modelling technique for diffusion of crystallographic migrating defects in irradiated metals and absorption by sinks to better predict the microstructural evolution in those materials.The phase field technique is well suited for this problem, since it naturally takes into account the elastic effects of dislocations on point defect diffusion in the most complex cases. The phase field model presented in this work has been adapted to simulate the generation of defects by irradiation and their absorption by the dislocation cores by means of a new order parameter associated to the sink morphology. The method has first been validated in different reference cases by comparing the sink strengths obtained numerically with analytical solutions available in the literature. Then, the method has been applied to dislocations with different orientations in zirconium, taking into account the anisotropic properties of the crystal and point defects, obtained by state-of-the-art atomic calculations.The results show that the shape anisotropy of the point defects promotes the vacancy absorption by basal loops, which is consistent with the experimentally observed zirconium growth under irradiation. Finally, the rigorous investigation of the dislocation loop case proves that phase field simulations give more accurate results than analytical solutions in realistic loop density ranges. (author)

  14. Phase diagrams of a spin-1/2 transverse Ising model with three-peak random field distribution

    International Nuclear Information System (INIS)

    Bassir, A.; Bassir, C.E.; Benyoussef, A.; Ez-Zahraouy, H.

    1996-07-01

    The effect of the transverse magnetic field on the phase diagrams structures of the Ising model in a random longitudinal magnetic field with a trimodal symmetric distribution is investigated within a finite cluster approximation. We find that a small magnetizations ordered phase (small ordered phase) disappears completely for a sufficiently large value of the transverse field or/and large value of the concentration of the disorder of the magnetic field. Multicritical behaviour and reentrant phenomena are discussed. The regions where the tricritical, reentrant phenomena and the small ordered phase persist are delimited as a function of the transverse field and the concentration p. Longitudinal magnetizations are also presented. (author). 33 refs, 6 figs

  15. Groundwater flow modelling under ice sheet conditions in Greenland (phase II)

    International Nuclear Information System (INIS)

    Jaquet, Olivier; Namar, Rabah; Siegel, Pascal; Jansson, Peter

    2012-11-01

    Within the framework of the GAP project, this second phase of geosphere modelling has enabled the development of an improved regional model that has led to a better representation of groundwater flow conditions likely to occur under ice sheet conditions. New data in relation to talik geometry and elevation, as well as to deformation zones were integrated in the geosphere model. In addition, more realistic hydraulic properties were considered for geosphere modelling; they were taken from the Laxemar site in Sweden. The geological medium with conductive deformation zones was modelled as a 3D continuum with stochastically hydraulic properties. Surface and basal glacial meltwater rates provided by a dynamic ice sheet model were assimilated into the groundwater flow model using mixed boundary conditions. The groundwater flow system is considered to be governed by infiltration of glacial meltwater in heterogeneous faulted crystalline rocks in the presence of permafrost and taliks. The characterisation of the permafrost-depth distribution was achieved using a coupled description of flow and heat transfer under steady state conditions. Using glaciological concepts and satellite data, an improved stochastic model was developed for the description at regional scale for the subglacial permafrost distribution in correlation with ice velocity and bed elevation data. Finally, the production of glacial meltwater by the ice sheet was traced for the determination of its depth and lateral extent. The major improvements are related to the type and handling of the subglacial boundary conditions. The use of meltwater rates provided by an ice sheet model applied as input to a mixed boundary condition enables to produce a more plausible flow field in the Eastern part of the domain, in comparison to previous modelling results (Jaquet et al. 2010). In addition, the integration of all potential taliks within the modelled domain provides a better characterisation of the likely groundwater

  16. Groundwater flow modelling under ice sheet conditions in Greenland (phase II)

    Energy Technology Data Exchange (ETDEWEB)

    Jaquet, Olivier; Namar, Rabah; Siegel, Pascal [In2Earth Modelling Ltd, Lausanne (Switzerland); Jansson, Peter [Dept. of Physical Geography and Quaternary Geology, Stockholm Univ., Stockholm (Sweden)

    2012-11-15

    Within the framework of the GAP project, this second phase of geosphere modelling has enabled the development of an improved regional model that has led to a better representation of groundwater flow conditions likely to occur under ice sheet conditions. New data in relation to talik geometry and elevation, as well as to deformation zones were integrated in the geosphere model. In addition, more realistic hydraulic properties were considered for geosphere modelling; they were taken from the Laxemar site in Sweden. The geological medium with conductive deformation zones was modelled as a 3D continuum with stochastically hydraulic properties. Surface and basal glacial meltwater rates provided by a dynamic ice sheet model were assimilated into the groundwater flow model using mixed boundary conditions. The groundwater flow system is considered to be governed by infiltration of glacial meltwater in heterogeneous faulted crystalline rocks in the presence of permafrost and taliks. The characterisation of the permafrost-depth distribution was achieved using a coupled description of flow and heat transfer under steady state conditions. Using glaciological concepts and satellite data, an improved stochastic model was developed for the description at regional scale for the subglacial permafrost distribution in correlation with ice velocity and bed elevation data. Finally, the production of glacial meltwater by the ice sheet was traced for the determination of its depth and lateral extent. The major improvements are related to the type and handling of the subglacial boundary conditions. The use of meltwater rates provided by an ice sheet model applied as input to a mixed boundary condition enables to produce a more plausible flow field in the Eastern part of the domain, in comparison to previous modelling results (Jaquet et al. 2010). In addition, the integration of all potential taliks within the modelled domain provides a better characterisation of the likely groundwater

  17. The Asymptotic Behaviour of a Stochastic 3D LANS-α Model

    International Nuclear Information System (INIS)

    Caraballo, Tomas; Marquez-Duran, Antonio M.; Real, Jose

    2006-01-01

    The long-time behaviour of a stochastic 3D LANS-α model on a bounded domain is analysed. First, we reformulate the model as an abstract problem. Next, we establish sufficient conditions ensuring the existence of stationary (steady state) solutions of this abstract nonlinear stochastic evolution equation, and study the stability properties of the model. Finally, we analyse the effects produced by stochastic perturbations in the deterministic version of the system (persistence of exponential stability as well as possible stabilisation effects produced by the noise). The general results are applied to our stochastic LANS-α system throughout the paper

  18. Fitting PAC spectra with stochastic models: PolyPacFit

    Energy Technology Data Exchange (ETDEWEB)

    Zacate, M. O., E-mail: zacatem1@nku.edu [Northern Kentucky University, Department of Physics and Geology (United States); Evenson, W. E. [Utah Valley University, College of Science and Health (United States); Newhouse, R.; Collins, G. S. [Washington State University, Department of Physics and Astronomy (United States)

    2010-04-15

    PolyPacFit is an advanced fitting program for time-differential perturbed angular correlation (PAC) spectroscopy. It incorporates stochastic models and provides robust options for customization of fits. Notable features of the program include platform independence and support for (1) fits to stochastic models of hyperfine interactions, (2) user-defined constraints among model parameters, (3) fits to multiple spectra simultaneously, and (4) any spin nuclear probe.

  19. A stochastic model for quantum measurement

    International Nuclear Information System (INIS)

    Budiyono, Agung

    2013-01-01

    We develop a statistical model of microscopic stochastic deviation from classical mechanics based on a stochastic process with a transition probability that is assumed to be given by an exponential distribution of infinitesimal stationary action. We apply the statistical model to stochastically modify a classical mechanical model for the measurement of physical quantities reproducing the prediction of quantum mechanics. The system+apparatus always has a definite configuration at all times, as in classical mechanics, fluctuating randomly following a continuous trajectory. On the other hand, the wavefunction and quantum mechanical Hermitian operator corresponding to the physical quantity arise formally as artificial mathematical constructs. During a single measurement, the wavefunction of the whole system+apparatus evolves according to a Schrödinger equation and the configuration of the apparatus acts as the pointer of the measurement so that there is no wavefunction collapse. We will also show that while the outcome of each single measurement event does not reveal the actual value of the physical quantity prior to measurement, its average in an ensemble of identical measurements is equal to the average of the actual value of the physical quantity prior to measurement over the distribution of the configuration of the system. (paper)

  20. Electron transport in the stochastic fields of RFP ZT-40M

    International Nuclear Information System (INIS)

    Punjabi, A.; Verma, A.; Kim, Myung-Hee; Boozer, A.

    1991-01-01

    In this paper, we use our newly developed Monte Carlo Method to study the transport of electrons in stochastic magnetic fields of the device RFP ZT4OM. The results of this calculation will be compared with the Rechester-Rosenbluth scaling

  1. Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay

    International Nuclear Information System (INIS)

    Goychuk, Igor; Goychuk, Andriy

    2015-01-01

    We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. (paper)

  2. The cardiorespiratory interaction: a nonlinear stochastic model and its synchronization properties

    Science.gov (United States)

    Bahraminasab, A.; Kenwright, D.; Stefanovska, A.; McClintock, P. V. E.

    2007-06-01

    We address the problem of interactions between the phase of cardiac and respiration oscillatory components. The coupling between these two quantities is experimentally investigated by the theory of stochastic Markovian processes. The so-called Markov analysis allows us to derive nonlinear stochastic equations for the reconstruction of the cardiorespiratory signals. The properties of these equations provide interesting new insights into the strength and direction of coupling which enable us to divide the couplings to two parts: deterministic and stochastic. It is shown that the synchronization behaviors of the reconstructed signals are statistically identical with original one.

  3. The critical domain size of stochastic population models.

    Science.gov (United States)

    Reimer, Jody R; Bonsall, Michael B; Maini, Philip K

    2017-02-01

    Identifying the critical domain size necessary for a population to persist is an important question in ecology. Both demographic and environmental stochasticity impact a population's ability to persist. Here we explore ways of including this variability. We study populations with distinct dispersal and sedentary stages, which have traditionally been modelled using a deterministic integrodifference equation (IDE) framework. Individual-based models (IBMs) are the most intuitive stochastic analogues to IDEs but yield few analytic insights. We explore two alternate approaches; one is a scaling up to the population level using the Central Limit Theorem, and the other a variation on both Galton-Watson branching processes and branching processes in random environments. These branching process models closely approximate the IBM and yield insight into the factors determining the critical domain size for a given population subject to stochasticity.

  4. Constitutive modeling of two phase materials using the Mean Field method for homogenization

    NARCIS (Netherlands)

    Perdahcioglu, Emin Semih; Geijselaers, Hubertus J.M.

    2010-01-01

    A Mean-Field homogenization framework for constitutive modeling of materials involving two distinct elastic-plastic phases is presented. With this approach it is possible to compute the macroscopic mechanical behavior of this type of materials based on the constitutive models of the constituent

  5. An adaptive meshfree method for phase-field models of biomembranes. Part I: Approximation with maximum-entropy basis functions

    OpenAIRE

    Rosolen, A.; Peco, C.; Arroyo, M.

    2013-01-01

    We present an adaptive meshfree method to approximate phase-field models of biomembranes. In such models, the Helfrich curvature elastic energy, the surface area, and the enclosed volume of a vesicle are written as functionals of a continuous phase-field, which describes the interface in a smeared manner. Such functionals involve up to second-order spatial derivatives of the phase-field, leading to fourth-order Euler–Lagrange partial differential equations (PDE). The solutions develop sharp i...

  6. Alternative Approaches to Technical Efficiency Estimation in the Stochastic Frontier Model

    OpenAIRE

    Acquah, H. de-Graft; Onumah, E. E.

    2014-01-01

    Estimating the stochastic frontier model and calculating technical efficiency of decision making units are of great importance in applied production economic works. This paper estimates technical efficiency from the stochastic frontier model using Jondrow, and Battese and Coelli approaches. In order to compare alternative methods, simulated data with sample sizes of 60 and 200 are generated from stochastic frontier model commonly applied to agricultural firms. Simulated data is employed to co...

  7. Towards a phase field model of the microstructural evolution of duplex steel with experimental verification

    DEFF Research Database (Denmark)

    Poulsen, Stefan Othmar; Voorhees, P.W.; Lauridsen, Erik Mejdal

    2012-01-01

    A phase field model to study the microstructural evolution of a polycrystalline dual-phase material with conserved phase fraction has been implemented, and 2D simulations have been performed. For 2D simulations, the model predicts the cubic growth well-known for diffusion-controlled systems. Some...... interphase boundaries are found to show a persistent non-constant curvature, which seems to be a feature of multi-phase materials. Finally, it is briefly outlined how this model is to be applied to investigate microstructural evolution in duplex steel. © (2012) Trans Tech Publications, Switzerland....

  8. Stochastic Modelling of Shiroro River Stream flow Process

    OpenAIRE

    Musa, J. J

    2013-01-01

    Economists, social scientists and engineers provide insights into the drivers of anthropogenic climate change and the options for adaptation and mitigation, and yet other scientists, including geographers and biologists, study the impacts of climate change. This project concentrates mainly on the discharge from the Shiroro River. A stochastic approach is presented for modeling a time series by an Autoregressive Moving Average model (ARMA). The development and use of a stochastic stream flow m...

  9. TIME-DEPENDENT STOCHASTIC ACCELERATION MODEL FOR FERMI BUBBLES

    Energy Technology Data Exchange (ETDEWEB)

    Sasaki, Kento; Asano, Katsuaki; Terasawa, Toshio, E-mail: kentos@icrr.u-tokyo.ac.jp, E-mail: asanok@icrr.u-tokyo.ac.jp, E-mail: terasawa@icrr.u-tokyo.ac.jp [Institute for Cosmic Ray Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8582 (Japan)

    2015-12-01

    We study stochastic acceleration models for the Fermi bubbles. Turbulence is excited just behind the shock front via Kelvin–Helmholtz, Rayleigh–Taylor, or Richtmyer–Meshkov instabilities, and plasma particles are continuously accelerated by the interaction with the turbulence. The turbulence gradually decays as it goes away from the shock fronts. Adopting a phenomenological model for the stochastic acceleration, we explicitly solve the temporal evolution of the particle energy distribution in the turbulence. Our results show that the spatial distribution of high-energy particles is different from those for a steady solution. We also show that the contribution of electrons that escaped from the acceleration regions significantly softens the photon spectrum. The photon spectrum and surface brightness profile are reproduced by our models. If the escape efficiency is very high, the radio flux from the escaped low-energy electrons can be comparable to that of the WMAP haze. We also demonstrate hadronic models with the stochastic acceleration, but they are unlikely in the viewpoint of the energy budget.

  10. Computer Aided Continuous Time Stochastic Process Modelling

    DEFF Research Database (Denmark)

    Kristensen, N.R.; Madsen, Henrik; Jørgensen, Sten Bay

    2001-01-01

    A grey-box approach to process modelling that combines deterministic and stochastic modelling is advocated for identification of models for model-based control of batch and semi-batch processes. A computer-aided tool designed for supporting decision-making within the corresponding modelling cycle...

  11. Review of "Stochastic Modelling for Systems Biology" by Darren Wilkinson

    Directory of Open Access Journals (Sweden)

    Bullinger Eric

    2006-12-01

    Full Text Available Abstract "Stochastic Modelling for Systems Biology" by Darren Wilkinson introduces the peculiarities of stochastic modelling in biology. This book is particularly suited to as a textbook or for self-study, and for readers with a theoretical background.

  12. Application of stochastic Galerkin FEM to the complete electrode model of electrical impedance tomography

    International Nuclear Information System (INIS)

    Leinonen, Matti; Hakula, Harri; Hyvönen, Nuutti

    2014-01-01

    The aim of electrical impedance tomography is to determine the internal conductivity distribution of some physical body from boundary measurements of current and voltage. The most accurate forward model for impedance tomography is the complete electrode model, which consists of the conductivity equation coupled with boundary conditions that take into account the electrode shapes and the contact resistances at the corresponding interfaces. If the reconstruction task of impedance tomography is recast as a Bayesian inference problem, it is essential to be able to solve the complete electrode model forward problem with the conductivity and the contact resistances treated as a random field and random variables, respectively. In this work, we apply a stochastic Galerkin finite element method to the ensuing elliptic stochastic boundary value problem and compare the results with Monte Carlo simulations

  13. Can a microscopic stochastic model explain the emergence of pain cycles in patients?

    International Nuclear Information System (INIS)

    Di Patti, Francesca; Fanelli, Duccio

    2009-01-01

    A stochastic model is introduced here to investigate the molecular mechanisms which trigger the perception of pain. The action of analgesic drug compounds is discussed in a dynamical context, where the competition with inactive species is explicitly accounted for. Finite size effects inevitably perturb the mean-field dynamics: oscillations in the amount of bound receptors are spontaneously manifested, driven by the noise which is intrinsic to the system under scrutiny. These effects are investigated both numerically, via stochastic simulations, and analytically, through a large size expansion. The claim that our findings could provide a consistent interpretative framework for explaining the emergence of cyclic behaviors in response to analgesic treatments is substantiated

  14. Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.

    Science.gov (United States)

    Schaff, James C; Gao, Fei; Li, Ye; Novak, Igor L; Slepchenko, Boris M

    2016-12-01

    Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.

  15. Stochastic Modelling of River Geometry

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Schaarup-Jensen, K.

    1996-01-01

    Numerical hydrodynamic river models are used in a large number of applications to estimate critical events for rivers. These estimates are subject to a number of uncertainties. In this paper, the problem to evaluate these estimates using probabilistic methods is considered. Stochastic models for ...... for river geometries are formulated and a coupling between hydraulic computational methods and numerical reliability methods is presented....

  16. Stochastic quantization and gravity

    International Nuclear Information System (INIS)

    Rumpf, H.

    1984-01-01

    We give a preliminary account of the application of stochastic quantization to the gravitational field. We start in Section I from Nelson's formulation of quantum mechanics as Newtonian stochastic mechanics and only then introduce the Parisi-Wu stochastic quantization scheme on which all the later discussion will be based. In Section II we present a generalization of the scheme that is applicable to fields in physical (i.e. Lorentzian) space-time and treat the free linearized gravitational field in this manner. The most remarkable result of this is the noncausal propagation of conformal gravitons. Moreover the concept of stochastic gauge-fixing is introduced and a complete discussion of all the covariant gauges is given. A special symmetry relating two classes of covariant gauges is exhibited. Finally Section III contains some preliminary remarks on full nonlinear gravity. In particular we argue that in contrast to gauge fields the stochastic gravitational field cannot be transformed to a Gaussian process. (Author)

  17. Modeling the photochemical attenuation of down-the-drain chemicals during river transport by stochastic methods and field measurements of pharmaceuticals and personal care products.

    Science.gov (United States)

    Hanamoto, Seiya; Nakada, Norihide; Yamashita, Naoyuki; Tanaka, Hiroaki

    2013-01-01

    Existing stochastic models for predicting concentrations of down-the-drain chemicals in aquatic environments do not account for the diurnal variation of direct photolysis by sunlight, despite its being an important factor in natural attenuation. To overcome this limitation, we developed a stochastic model incorporating temporal variations in direct photolysis. To verify the model, we measured 57 pharmaceuticals and personal care products (PPCPs) in a 7.6-km stretch of an urban river, and determined their physical and biological properties in laboratory experiments. During transport along the river, 8 PPCPs, including ketoprofen and azithromycin, were attenuated by >20%, mainly owing to direct photolysis and adsorption to sediments. The photolabile PPCPs attenuated significantly in the daytime but persisted in the nighttime. The observations were similar to the values predicted by the photolysis model for the photolabile PPCPs (i.e., ketoprofen, diclofenac and furosemide) but not by the existing model. The stochastic model developed in this study was suggested to be a novel and useful stochastic model for evaluating direct photolysis of down-the-drain chemicals, which occurs during the river transport.

  18. Stochastic Volatility and DSGE Models

    DEFF Research Database (Denmark)

    Andreasen, Martin Møller

    This paper argues that a specification of stochastic volatility commonly used to analyze the Great Moderation in DSGE models may not be appropriate, because the level of a process with this specification does not have conditional or unconditional moments. This is unfortunate because agents may...

  19. Stochastic modeling of catalytic processes in nanoporous materials: Beyond mean-field approach

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, Andres [Iowa State Univ., Ames, IA (United States)

    2017-08-05

    Transport and reaction in zeolites and other porous materials, such as mesoporous silica particles, has been a focus of interest in recent years. This is in part due to the possibility of anomalous transport effects (e.g. single-file diffusion) and its impact in the reaction yield in catalytic processes. Computational simulations are often used to study these complex nonequilibrium systems. Computer simulations using Molecular Dynamics (MD) techniques are prohibitive, so instead coarse grained one-dimensional models with the aid of Kinetic Monte Carlo (KMC) simulations are used. Both techniques can be computationally expensive, both time and resource wise. These coarse-grained systems can be exactly described by a set of coupled stochastic master equations, that describe the reaction-diffusion kinetics of the system. The equations can be written exactly, however, coupling between the equations and terms within the equations make it impossible to solve them exactly; approximations must be made. One of the most common methods to obtain approximate solutions is to use Mean Field (MF) theory. MF treatments yield reasonable results at high ratios of reaction rate k to hop rate h of the particles, but fail completely at low k=h due to the over-estimation of fluxes of particles within the pore. We develop a method to estimate fluxes and intrapore diffusivity in simple one- dimensional reaction-diffusion models at high and low k=h, where the pores are coupled to an equilibrated three-dimensional fluid. We thus successfully describe analytically these simple reaction-diffusion one-dimensional systems. Extensions to models considering behavior with long range steric interactions and wider pores require determination of multiple boundary conditions. We give a prescription to estimate the required parameters for these simulations. For one dimensional systems, if single-file diffusion is relaxed, additional parameters to describe particle exchange have to be introduced. We use

  20. Stability analysis of multi-group deterministic and stochastic epidemic models with vaccination rate

    International Nuclear Information System (INIS)

    Wang Zhi-Gang; Gao Rui-Mei; Fan Xiao-Ming; Han Qi-Xing

    2014-01-01

    We discuss in this paper a deterministic multi-group MSIR epidemic model with a vaccination rate, the basic reproduction number ℛ 0 , a key parameter in epidemiology, is a threshold which determines the persistence or extinction of the disease. By using Lyapunov function techniques, we show if ℛ 0 is greater than 1 and the deterministic model obeys some conditions, then the disease will prevail, the infective persists and the endemic state is asymptotically stable in a feasible region. If ℛ 0 is less than or equal to 1, then the infective disappear so the disease dies out. In addition, stochastic noises around the endemic equilibrium will be added to the deterministic MSIR model in order that the deterministic model is extended to a system of stochastic ordinary differential equations. In the stochastic version, we carry out a detailed analysis on the asymptotic behavior of the stochastic model. In addition, regarding the value of ℛ 0 , when the stochastic system obeys some conditions and ℛ 0 is greater than 1, we deduce the stochastic system is stochastically asymptotically stable. Finally, the deterministic and stochastic model dynamics are illustrated through computer simulations. (general)

  1. Stochastic differential equations used to model conjugation

    DEFF Research Database (Denmark)

    Philipsen, Kirsten Riber; Christiansen, Lasse Engbo

    Stochastic differential equations (SDEs) are used to model horizontal transfer of antibiotic resis- tance by conjugation. The model describes the concentration of donor, recipient, transconjugants and substrate. The strength of the SDE model over the traditional ODE models is that the noise can...

  2. A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis

    Directory of Open Access Journals (Sweden)

    Linda J.S. Allen

    2017-05-01

    Full Text Available Some mathematical methods for formulation and numerical simulation of stochastic epidemic models are presented. Specifically, models are formulated for continuous-time Markov chains and stochastic differential equations. Some well-known examples are used for illustration such as an SIR epidemic model and a host-vector malaria model. Analytical methods for approximating the probability of a disease outbreak are also discussed. Keywords: Branching process, Continuous-time Markov chain, Minor outbreak, Stochastic differential equation, 2000 MSC: 60H10, 60J28, 92D30

  3. On a stochastic process associated to non-abelian gauge fields

    International Nuclear Information System (INIS)

    Vilela Mendes, R.

    1989-01-01

    A stochastic process is constructed from a ground state measure that generalizes to non-abelian fields the ground state of abelian (free) gauge fields without fermions. Using a latticized version one shows how the process leads to a well-defined quantum theory in the Schroedinger representation. An analysis of the qualitative behaviour of the theory seems to imply a quasi-free behaviour at short distances and a maximally disordered field strength configuration for the low-momentum component of the ground state. Scaling relations for the mass gap are inferred from the theory of small random perturbations of dynamical systems. (orig.)

  4. Stochastic modeling of near-field exposure to parabens in personal care products

    DEFF Research Database (Denmark)

    Csiszar, Susan A.; Ernstoff, Alexi; Fantke, Peter

    2017-01-01

    Exposure assessment is a key step in determining risks to chemicals in consumer goods, including personal care products (PCPs). Exposure models can be used to estimate exposures to chemicals in the absence of biomonitoring data and as tools in chemical risk prioritization and screening. We apply...... a PCP exposure model based on the product intake fraction (PiF), which is defined as the fraction of chemical in a product that is taken in by the exposed population, to estimate chemical intake based on physicochemical properties and PCP usage characteristics. The PiF can be used to estimate route...... and pathway-specific exposures during both the use and disposal stages of a product. As a case study, we stochastically quantified population level exposures to parabens in PCPs, and compared estimates with biomarker values. We estimated exposure based on the usage of PCPs in the female US population, taking...

  5. Collective, stochastic and nonequilibrium behavior of highly excited hadronic matter

    Energy Technology Data Exchange (ETDEWEB)

    Carruthers, P [Los Alamos National Lab., NM (USA). Theoretical Div.

    1984-04-23

    We discuss selected problems concerning the dynamics and stochastic behavior of highly excited matter, particularly the QCD plasma. For the latter we consider the equation of state, kinetics, quasiparticles, flow properties and possible chaos and turbulence. The promise of phase space distribution functions for covariant transport and kinetic theory is stressed. The possibility and implications of a stochastic bag are spelled out. A simplified space-time model of hadronic collisions is pursued, with applications to A-A collisions and other matters. The domain wall between hadronic and plasma phase is of potential importance: its thickness and relation to surface tension is noticed. Finally, we review the recently developed stochastic cell model of multiparticle distributions and KNO scaling. This topic leads to the notion that fractional dimensions are involved in a rather general dynamical context. We speculate that various scaling phenomena are independent of the full dynamical structure, depending only on a general stochastic framework having to do with simple maps and strange attractors. 42 refs.

  6. Some Remarks on Stochastic Versions of the Ramsey Growth Model

    Czech Academy of Sciences Publication Activity Database

    Sladký, Karel

    2012-01-01

    Roč. 19, č. 29 (2012), s. 139-152 ISSN 1212-074X R&D Projects: GA ČR GAP402/10/1610; GA ČR GAP402/10/0956; GA ČR GAP402/11/0150 Institutional support: RVO:67985556 Keywords : Economic dynamics * Ramsey growth model with disturbance * stochastic dynamic programming * multistage stochastic programs Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/E/sladky-some remarks on stochastic versions of the ramsey growth model.pdf

  7. Stochastic modeling of consumer preferences for health care institutions.

    Science.gov (United States)

    Malhotra, N K

    1983-01-01

    This paper proposes a stochastic procedure for modeling consumer preferences via LOGIT analysis. First, a simple, non-technical exposition of the use of a stochastic approach in health care marketing is presented. Second, a study illustrating the application of the LOGIT model in assessing consumer preferences for hospitals is given. The paper concludes with several implications of the proposed approach.

  8. Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly

    Energy Technology Data Exchange (ETDEWEB)

    Eugène, Sarah, E-mail: Sarah.Eugene@inria.fr; Doumic, Marie, E-mail: Philippe.Robert@inria.fr, E-mail: Marie.Doumic@inria.fr [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France); Sorbonne Universités, UPMC Université Pierre et Marie Curie, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Xue, Wei-Feng, E-mail: W.F.Xue@kent.ac.uk [School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ (United Kingdom); Robert, Philippe, E-mail: Philippe.Robert@inria.fr [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France)

    2016-05-07

    Self-assembly of proteins into amyloid aggregates is an important biological phenomenon associated with human diseases such as Alzheimer’s disease. Amyloid fibrils also have potential applications in nano-engineering of biomaterials. The kinetics of amyloid assembly show an exponential growth phase preceded by a lag phase, variable in duration as seen in bulk experiments and experiments that mimic the small volumes of cells. Here, to investigate the origins and the properties of the observed variability in the lag phase of amyloid assembly currently not accounted for by deterministic nucleation dependent mechanisms, we formulate a new stochastic minimal model that is capable of describing the characteristics of amyloid growth curves despite its simplicity. We then solve the stochastic differential equations of our model and give mathematical proof of a central limit theorem for the sample growth trajectories of the nucleated aggregation process. These results give an asymptotic description for our simple model, from which closed form analytical results capable of describing and predicting the variability of nucleated amyloid assembly were derived. We also demonstrate the application of our results to inform experiments in a conceptually friendly and clear fashion. Our model offers a new perspective and paves the way for a new and efficient approach on extracting vital information regarding the key initial events of amyloid formation.

  9. Stochastic ontogenetic growth model

    Science.gov (United States)

    West, B. J.; West, D.

    2012-02-01

    An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.

  10. Gray and multigroup radiation transport models for two-dimensional binary stochastic media using effective opacities

    International Nuclear Information System (INIS)

    Olson, Gordon L.

    2016-01-01

    One-dimensional models for the transport of radiation through binary stochastic media do not work in multi-dimensions. Authors have attempted to modify or extend the 1D models to work in multidimensions without success. Analytic one-dimensional models are successful in 1D only when assuming greatly simplified physics. State of the art theories for stochastic media radiation transport do not address multi-dimensions and temperature-dependent physics coefficients. Here, the concept of effective opacities and effective heat capacities is found to well represent the ensemble averaged transport solutions in cases with gray or multigroup temperature-dependent opacities and constant or temperature-dependent heat capacities. In every case analyzed here, effective physics coefficients fit the transport solutions over a useful range of parameter space. The transport equation is solved with the spherical harmonics method with angle orders of n=1 and 5. Although the details depend on what order of solution is used, the general results are similar, independent of angular order. - Highlights: • Gray and multigroup radiation transport is done through 2D stochastic media. • Approximate models for the mean radiation field are found for all test problems. • Effective opacities are adjusted to fit the means of stochastic media transport. • Test problems include temperature dependent opacities and heat capacities • Transport solutions are done with angle orders n=1 and 5.

  11. Multivariate moment closure techniques for stochastic kinetic models

    International Nuclear Information System (INIS)

    Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-01-01

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs

  12. Multivariate moment closure techniques for stochastic kinetic models

    Energy Technology Data Exchange (ETDEWEB)

    Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H., E-mail: m.stumpf@imperial.ac.uk [Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ (United Kingdom)

    2015-09-07

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  13. Markov stochasticity coordinates

    International Nuclear Information System (INIS)

    Eliazar, Iddo

    2017-01-01

    Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.

  14. Markov stochasticity coordinates

    Energy Technology Data Exchange (ETDEWEB)

    Eliazar, Iddo, E-mail: iddo.eliazar@intel.com

    2017-01-15

    Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.

  15. Phase-field modeling of the microstructure evolution and heterogeneous nucleation in solidifying ternary Al–Cu–Ni alloys

    International Nuclear Information System (INIS)

    Kundin, Julia; Pogorelov, Evgeny; Emmerich, Heike

    2015-01-01

    We have investigated the microstructure evolution during the isothermal and non-isothermal solidification of ternary Al–Cu–Ni alloys by means of a general multi-phase-field model for an arbitrary number of phases. The stability requirements for the model functions on every dual interface guarantee the absence of “ghost” phases. The aim was to generate a realistic microstructure by coupling the thermodynamic parameters of the phases and the thermodynamically consistent phase-field evolution equations. It is shown that the specially constructed thermal noise terms disturb the stability on the dual interfaces and can produce heterogeneous nucleation of product phases at energetically favorable points. Similar behavior can be observed in triple junctions where the heterogeneous nucleation of a fourth phase is more favorable. Finally, the model predicts the growth of a combined eutectic-like and peritectic-like structure that is comparable to the observed experimental microstructure in various alloys

  16. Comparison of stochastic models in Monte Carlo simulation of coated particle fuels

    International Nuclear Information System (INIS)

    Yu Hui; Nam Zin Cho

    2013-01-01

    There is growing interest worldwide in very high temperature gas cooled reactors as candidates for next generation reactor systems. For design and analysis of such reactors with double heterogeneity introduced by the coated particle fuels that are randomly distributed in graphite pebbles, stochastic transport models are becoming essential. Several models were reported in the literature, such as coarse lattice models, fine lattice stochastic (FLS) models, random sequential addition (RSA) models, metropolis models. The principles and performance of these stochastic models are described and compared in this paper. Compared with the usual fixed lattice methods, sub-FLS modeling allows more realistic stochastic distribution of fuel particles and thus results in more accurate criticality calculation. Compared with the basic RSA method, sub-FLS modeling requires simpler and more efficient overlapping checking procedure. (authors)

  17. Compositional Modelling of Stochastic Hybrid Systems

    NARCIS (Netherlands)

    Strubbe, S.N.

    2005-01-01

    In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete

  18. Stochastic models for turbulent reacting flows

    Energy Technology Data Exchange (ETDEWEB)

    Kerstein, A. [Sandia National Laboratories, Livermore, CA (United States)

    1993-12-01

    The goal of this program is to develop and apply stochastic models of various processes occurring within turbulent reacting flows in order to identify the fundamental mechanisms governing these flows, to support experimental studies of these flows, and to further the development of comprehensive turbulent reacting flow models.

  19. Multi-scenario modelling of uncertainty in stochastic chemical systems

    International Nuclear Information System (INIS)

    Evans, R. David; Ricardez-Sandoval, Luis A.

    2014-01-01

    Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo

  20. A stochastic step flow model with growth in 1+1 dimensions

    International Nuclear Information System (INIS)

    Margetis, Dionisios

    2010-01-01

    Mathematical implications of adding Gaussian white noise to the Burton-Cabrera-Frank model for N terraces ('gaps') on a crystal surface are studied under external material deposition for large N. The terraces separate straight, non-interacting line defects (steps) with uniform spacing initially (t = 0). As the growth tends to vanish, the gaps become uncorrelated. First, simple closed-form expressions for the gap variance are obtained directly for small fluctuations. The leading-order, linear stochastic differential equations are prototypical for discrete asymmetric processes. Second, the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy for joint gap densities is formulated. Third, a self-consistent 'mean field' is defined via the BBGKY hierarchy. This field is then determined approximately through a terrace decorrelation hypothesis. Fourth, comparisons are made of directly obtained and mean-field results. Limitations and issues in the modeling of noise are outlined.

  1. A new stochastic model considering satellite clock interpolation errors in precise point positioning

    Science.gov (United States)

    Wang, Shengli; Yang, Fanlin; Gao, Wang; Yan, Lizi; Ge, Yulong

    2018-03-01

    Precise clock products are typically interpolated based on the sampling interval of the observational data when they are used for in precise point positioning. However, due to the occurrence of white noise in atomic clocks, a residual component of such noise will inevitable reside within the observations when clock errors are interpolated, and such noise will affect the resolution of the positioning results. In this paper, which is based on a twenty-one-week analysis of the atomic clock noise characteristics of numerous satellites, a new stochastic observation model that considers satellite clock interpolation errors is proposed. First, the systematic error of each satellite in the IGR clock product was extracted using a wavelet de-noising method to obtain the empirical characteristics of atomic clock noise within each clock product. Then, based on those empirical characteristics, a stochastic observation model was structured that considered the satellite clock interpolation errors. Subsequently, the IGR and IGS clock products at different time intervals were used for experimental validation. A verification using 179 stations worldwide from the IGS showed that, compared with the conventional model, the convergence times using the stochastic model proposed in this study were respectively shortened by 4.8% and 4.0% when the IGR and IGS 300-s-interval clock products were used and by 19.1% and 19.4% when the 900-s-interval clock products were used. Furthermore, the disturbances during the initial phase of the calculation were also effectively improved.

  2. Study on individual stochastic model of GNSS observations for precise kinematic applications

    Science.gov (United States)

    Próchniewicz, Dominik; Szpunar, Ryszard

    2015-04-01

    The proper definition of mathematical positioning model, which is defined by functional and stochastic models, is a prerequisite to obtain the optimal estimation of unknown parameters. Especially important in this definition is realistic modelling of stochastic properties of observations, which are more receiver-dependent and time-varying than deterministic relationships. This is particularly true with respect to precise kinematic applications which are characterized by weakening model strength. In this case, incorrect or simplified definition of stochastic model causes that the performance of ambiguity resolution and accuracy of position estimation can be limited. In this study we investigate the methods of describing the measurement noise of GNSS observations and its impact to derive precise kinematic positioning model. In particular stochastic modelling of individual components of the variance-covariance matrix of observation noise performed using observations from a very short baseline and laboratory GNSS signal generator, is analyzed. Experimental test results indicate that the utilizing the individual stochastic model of observations including elevation dependency and cross-correlation instead of assumption that raw measurements are independent with the same variance improves the performance of ambiguity resolution as well as rover positioning accuracy. This shows that the proposed stochastic assessment method could be a important part in complex calibration procedure of GNSS equipment.

  3. Galactic Cosmic-ray Transport in the Global Heliosphere: A Four-Dimensional Stochastic Model

    Science.gov (United States)

    Florinski, V.

    2009-04-01

    We study galactic cosmic-ray transport in the outer heliosphere and heliosheath using a newly developed transport model based on stochastic integration of the phase-space trajectories of Parker's equation. The model employs backward integration of the diffusion-convection transport equation using Ito calculus and is four-dimensional in space+momentum. We apply the model to the problem of galactic proton transport in the heliosphere during a negative solar minimum. Model results are compared with the Voyager measurements of galactic proton radial gradients and spectra in the heliosheath. We show that the heliosheath is not as efficient in diverting cosmic rays during solar minima as predicted by earlier two-dimensional models.

  4. Modeling collective emotions: a stochastic approach based on Brownian agents

    International Nuclear Information System (INIS)

    Schweitzer, F.

    2010-01-01

    We develop a agent-based framework to model the emergence of collective emotions, which is applied to online communities. Agents individual emotions are described by their valence and arousal. Using the concept of Brownian agents, these variables change according to a stochastic dynamics, which also considers the feedback from online communication. Agents generate emotional information, which is stored and distributed in a field modeling the online medium. This field affects the emotional states of agents in a non-linear manner. We derive conditions for the emergence of collective emotions, observable in a bimodal valence distribution. Dependent on a saturated or a super linear feedback between the information field and the agent's arousal, we further identify scenarios where collective emotions only appear once or in a repeated manner. The analytical results are illustrated by agent-based computer simulations. Our framework provides testable hypotheses about the emergence of collective emotions, which can be verified by data from online communities. (author)

  5. Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

    Science.gov (United States)

    Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

    2006-06-01

    In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

  6. Insights into pre-reversal paleosecular variation from stochastic models

    Directory of Open Access Journals (Sweden)

    Klaudio ePeqini

    2015-09-01

    Full Text Available To provide insights on the paleosecular variation of the geomagnetic field and the mechanism of reversals, long time series of the dipolar magnetic moment are generated by two different stochastic models, known as the domino model and the inhomogeneous Lebovitz disk dynamo model, with initial values taken from the from paleomagnetic data. The former model considers mutual interactions of N macrospins embedded in a uniformly rotating medium, where random forcing and dissipation act on each macrospin. With an appropriate set of the model’s parameters values, the series generated by this model have similar statistical behaviour to the time series of the SHA.DIF.14K model. The latter model is an extension of the classical two-disk Rikitake model, considering N dynamo elements with appropriate interactions between them.We varied the parameters set of both models aiming at generating suitable time series with behaviour similar to the long time series of recent secular variation (SV. Such series are then extended to the near future, obtaining reversals in both cases of models. The analysis of the time series generated by simulating the models show that the reversals appears after a persistent period of low intensity geomagnetic field, as it is occurring in the present times.

  7. Simulation of spheroidisation of elongated Si-particle in Al-Si alloys by the phase-field model

    International Nuclear Information System (INIS)

    Kovacevic, I.

    2008-01-01

    The application of the phase-field model for spheroidisation of undissolvable particles during high-temperature treatment of alloys is pointed out. Modelling of the spheroidisation of elongated Si-particles during annealing of Al-Si alloy is elaborated in this paper. The driving force for spheroidisation is the minimization of the total free-energy of the system or the minimization of the ratio between the interface areas and the particle volumes. The spheroidisation kinetics of elongated Si-particle for binary Al-Si system during homogenisation of aluminium alloys simulated by the phase-field model is demonstrated. The influences of the interface energy and the homogenisation temperature on the spheroidisation kinetics is presented. The lack of knowledge of the interface energy anisotropy between Si-particle and the aluminium phase is the only reason for using isotropic interface energy in simulations. The thermodynamic driving force for the phase transformation of the silicon into the aluminium phase is computed from the data obtained from the JMatPro software for aluminium alloys

  8. A Stochastic Fractional Dynamics Model of Rainfall Statistics

    Science.gov (United States)

    Kundu, Prasun; Travis, James

    2013-04-01

    Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is designed to faithfully reflect the scale dependence and is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. The main restriction is the assumption that the statistics of the precipitation field is spatially homogeneous and isotropic and stationary in time. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of the radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment. Some data sets containing periods of non-stationary behavior that involves occasional anomalously correlated rain events, present a challenge for the model.

  9. Markov random fields simulation: an introduction to the stochastic modelling of petroleum reservoirs; Simulacao de campos aleatorios markovianos: uma introducao voltada a modelagem estocastica de reservatorios de petroleo

    Energy Technology Data Exchange (ETDEWEB)

    Saldanha Filho, Paulo Carlos

    1998-02-01

    Stochastic simulation has been employed in petroleum reservoir characterization as a modeling tool able to reconcile information from several different sources. It has the ability to preserve the variability of the modeled phenomena and permits transference of geological knowledge to numerical models of flux, whose predictions on reservoir constitute the main basis for reservoir management decisions. Several stochastic models have been used and/or suggested, depending on the nature of the phenomena to be described. Markov Random Fields (MRFs) appear as an alternative for the modeling of discrete variables, mainly reservoirs with mosaic architecture of facies. In this dissertation, the reader is introduced to the stochastic modeling by MRFs in a generic sense. The main aspects of the technique are reviewed. MRF Conceptual Background is described: its characterization through the Markovian property and the equivalence to Gibbs distributions. The framework for generic modeling of MRFs is described. The classical models of Ising and Potts-Strauss are specific in this context and are related to models of Ising and Potts-Strauss are specific in this context and are related to models used in petroleum reservoir characterization. The problem of parameter estimation is discussed. The maximum pseudolikelihood estimators for some models are presented. Estimators for two models useful for reservoir characterization are developed, and represent a new contribution to the subject. Five algorithms for the Conditional Simulation of MRFs are described: the Metropolis algorithm, the algorithm of German and German (Gibbs sampler), the algorithm of Swendsen-Wang, the algorithm of Wolff, and the algorithm of Flinn. Finally, examples of simulation for some of the models discussed are presented, along with their implications on the modelling of petroleum reservoirs. (author)

  10. Estimation of Stochastic Volatility Models by Nonparametric Filtering

    DEFF Research Database (Denmark)

    Kanaya, Shin; Kristensen, Dennis

    2016-01-01

    /estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...

  11. Effect of multiple Higgs fields on the phase structure of the SU(2)-Higgs model

    International Nuclear Information System (INIS)

    Wurtz, Mark; Steele, T. G.; Lewis, Randy

    2009-01-01

    The SU(2)-Higgs model, with a single Higgs field in the fundamental representation and a quartic self-interaction, has a Higgs region and a confinement region which are analytically connected in the parameter space of the theory; these regions thus represent a single phase. The effect of multiple Higgs fields on this phase structure is examined via Monte Carlo lattice simulations. For the case of N≥2 identical Higgs fields, there is no remaining analytic connection between the Higgs and confinement regions, at least when Lagrangian terms that directly couple different Higgs flavors are omitted. An explanation of this result in terms of enhancement from overlapping phase transitions is explored for N=2 by introducing an asymmetry in the hopping parameters of the Higgs fields. It is found that an enhancement of the phase transitions can still occur for a moderate (10%) asymmetry in the resulting hopping parameters.

  12. Application of Stochastic Unsaturated Flow Theory, Numerical Simulations, and Comparisons to Field Observations

    DEFF Research Database (Denmark)

    Jensen, Karsten Høgh; Mantoglou, Aristotelis

    1992-01-01

    unsaturated flow equation representing the mean system behavior is solved using a finite difference numerical solution technique. The effective parameters are evaluated from the stochastic theory formulas before entering them into the numerical solution for each iteration. The stochastic model is applied...... seems to offer a rational framework for modeling large-scale unsaturated flow and estimating areal averages of soil-hydrological processes in spatially variable soils....

  13. A Simulation-Based Dynamic Stochastic Route Choice Model for Evacuation

    Directory of Open Access Journals (Sweden)

    Xing Zhao

    2012-01-01

    Full Text Available This paper establishes a dynamic stochastic route choice model for evacuation to simulate the propagation process of traffic flow and estimate the stochastic route choice under evacuation situations. The model contains a lane-group-based cell transmission model (CTM which sets different traffic capacities for links with different turning movements to flow out in an evacuation situation, an actual impedance model which is to obtain the impedance of each route in time units at each time interval and a stochastic route choice model according to the probit-based stochastic user equilibrium. In this model, vehicles loading at each origin at each time interval are assumed to choose an evacuation route under determinate road network, signal design, and OD demand. As a case study, the proposed model is validated on the network nearby Nanjing Olympic Center after the opening ceremony of the 10th National Games of the People's Republic of China. The traffic volumes and clearing time at five exit points of the evacuation zone are calculated by the model to compare with survey data. The results show that this model can appropriately simulate the dynamic route choice and evolution process of the traffic flow on the network in an evacuation situation.

  14. On the small-time behavior of stochastic logistic models

    Directory of Open Access Journals (Sweden)

    Dung Tien Nguyen

    2017-09-01

    Full Text Available In this paper we investigate the small-time behaviors of the solution to  a stochastic logistic model. The obtained results allow us to estimate the number of individuals in the population and can be used to study stochastic prey-predator systems.

  15. NLP model and stochastic multi-start optimization approach for heat exchanger networks

    International Nuclear Information System (INIS)

    Núñez-Serna, Rosa I.; Zamora, Juan M.

    2016-01-01

    Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.

  16. Testing the robustness of deterministic models of optimal dynamic pricing and lot-sizing for deteriorating items under stochastic conditions

    DEFF Research Database (Denmark)

    Ghoreishi, Maryam

    2018-01-01

    Many models within the field of optimal dynamic pricing and lot-sizing models for deteriorating items assume everything is deterministic and develop a differential equation as the core of analysis. Two prominent examples are the papers by Rajan et al. (Manag Sci 38:240–262, 1992) and Abad (Manag......, we will try to expose the model by Abad (1996) and Rajan et al. (1992) to stochastic inputs; however, designing these stochastic inputs such that they as closely as possible are aligned with the assumptions of those papers. We do our investigation through a numerical test where we test the robustness...... of the numerical results reported in Rajan et al. (1992) and Abad (1996) in a simulation model. Our numerical results seem to confirm that the results stated in these papers are indeed robust when being imposed to stochastic inputs....

  17. Numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow

    KAUST Repository

    Zhu, Guangpu

    2018-04-17

    In this paper, we consider the numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow. The nonlinearly coupled model consists of two Cahn-Hilliard type equations and incompressible Navier-Stokes equations. Using the Invariant Energy Quadratization (IEQ) approach, the governing system is transformed into an equivalent form, which allows the nonlinear potentials to be treated efficiently and semi-explicitly. we construct a first and a second-order time marching schemes, which are extremely efficient and easy-to-implement, for the transformed governing system. At each time step, the schemes involve solving a sequence of linear elliptic equations, and computations of phase variables, velocity and pressure are totally decoupled. We further establish a rigorous proof of unconditional energy stability for the semi-implicit schemes. Numerical results in both two and three dimensions are obtained, which demonstrate that the proposed schemes are accurate, efficient and unconditionally energy stable. Using our schemes, we investigate the effect of surfactants on droplet deformation and collision under a shear flow. The increase of surfactant concentration can enhance droplet deformation and inhibit droplet coalescence.

  18. Numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow

    KAUST Repository

    Zhu, Guangpu; Kou, Jisheng; Sun, Shuyu; Yao, Jun; Li, Aifen

    2018-01-01

    In this paper, we consider the numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow. The nonlinearly coupled model consists of two Cahn-Hilliard type equations and incompressible Navier-Stokes equations. Using the Invariant Energy Quadratization (IEQ) approach, the governing system is transformed into an equivalent form, which allows the nonlinear potentials to be treated efficiently and semi-explicitly. we construct a first and a second-order time marching schemes, which are extremely efficient and easy-to-implement, for the transformed governing system. At each time step, the schemes involve solving a sequence of linear elliptic equations, and computations of phase variables, velocity and pressure are totally decoupled. We further establish a rigorous proof of unconditional energy stability for the semi-implicit schemes. Numerical results in both two and three dimensions are obtained, which demonstrate that the proposed schemes are accurate, efficient and unconditionally energy stable. Using our schemes, we investigate the effect of surfactants on droplet deformation and collision under a shear flow. The increase of surfactant concentration can enhance droplet deformation and inhibit droplet coalescence.

  19. Spatial stochastic regression modelling of urban land use

    International Nuclear Information System (INIS)

    Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A

    2014-01-01

    Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable

  20. arXiv Stochastic locality and master-field simulations of very large lattices

    CERN Document Server

    Lüscher, Martin

    2018-01-01

    In lattice QCD and other field theories with a mass gap, the field variables in distant regions of a physically large lattice are only weakly correlated. Accurate stochastic estimates of the expectation values of local observables may therefore be obtained from a single representative field. Such master-field simulations potentially allow very large lattices to be simulated, but require various conceptual and technical issues to be addressed. In this talk, an introduction to the subject is provided and some encouraging results of master-field simulations of the SU(3) gauge theory are reported.

  1. Phase-field-crystal model for magnetocrystalline interactions in isotropic ferromagnetic solids

    Science.gov (United States)

    Faghihi, Niloufar; Provatas, Nikolas; Elder, K. R.; Grant, Martin; Karttunen, Mikko

    2013-09-01

    An isotropic magnetoelastic phase-field-crystal model to study the relation between morphological structure and magnetic properties of pure ferromagnetic solids is introduced. Analytic calculations in two dimensions were used to determine the phase diagram and obtain the relationship between elastic strains and magnetization. Time-dependent numerical simulations in two dimensions were used to demonstrate the effect of grain boundaries on the formation of magnetic domains. It was shown that the grain boundaries act as nucleating sites for domains of reverse magnetization. Finally, we derive a relation for coercivity versus grain misorientation in the isotropic limit.

  2. The propagation of stochastic pixel noise into magnitude and phase values in the Fourier analysis of digital images

    International Nuclear Information System (INIS)

    Holden, J.E.; Halama, J.R.; Hasegawa, B.H.

    1986-01-01

    The use of Fourier analysis in nuclear medicine gated blood ventriculography provides a useful example of the application of Fourier methods to digital medical imaging. In particular, the nuclear medicine experience demonstrates that there is diagnostic significance not only in the pixel averages of temporal Fourier magnitude and phase computed in various image regions, but also in the distributions of the individual pixel values about those averages. However, a region containing pixels that are perfectly synchronous on average would still yield a finite distribution of calculated Fourier coefficients due to the propagation of stochastic pixel noise into the calculated values. The authors have studied this noise component of both the magnitude and phase distributions using phantom studies and computer simulation. In both approaches, several thousand one-pixel 'ventriculograms' were generated, all identical to each other except for stochastic noise. Fourier magnitudes and phases at several frequencies were calculated and histograms generated. A theoretical prediction of the distributions was developed and shown to fit the experimental results well. The authors' formalism can be used to estimate study count requirements or, for fixed study counts, to assess the stochastic noise contribution in the interpretation of measured phase and magnitude distributions. (author)

  3. Analysis of stochastic magnetic fields formed by the application of resonant magnetic perturbations on MAST and comparison with experiment

    International Nuclear Information System (INIS)

    Denner, P.; Liu, Yueqiang; Kirk, A.; Nardon, E.

    2012-01-01

    In MAST experiments with applied resonant magnetic perturbations (RMPs), clear reduction in line-averaged density has been observed in a wide range of L-mode plasmas when there is an alignment between the perturbation and the equilibrium magnetic field that maximizes the size of the resonant components of the applied magnetic field, as well as in a few H-mode plasmas but with a much stronger sensitivity to this alignment. This density pump-out is the result of increased particle transport, which is thought to be caused by the formation of a stochastic magnetic field in the plasma edge. This paper presents an analysis of the magnetic field structures formed by the application of n = 3 RMPs on MAST, including various parameters characterizing the degree of stochasticity in the plasma edge. Values for these parameters are calculated and compared with the amount of density pump-out observed in MAST experiments. It is found that density pump-out is fairly well correlated with some of the parameters calculated using vacuum modelling, but none of them provides a single threshold value for pump-out that applies to both L- and H-mode plasmas. Plasma response modelling provides a robust criterion for density pump-out that applies both to L- and H-mode plasmas. (paper)

  4. Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

    Science.gov (United States)

    Gay-Balmaz, François; Holm, Darryl D.

    2018-01-01

    Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.

  5. Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

    Science.gov (United States)

    Gay-Balmaz, François; Holm, Darryl D.

    2018-06-01

    Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.

  6. Markov Chain Models for the Stochastic Modeling of Pitting Corrosion

    Directory of Open Access Journals (Sweden)

    A. Valor

    2013-01-01

    Full Text Available The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the transition probability function in a discrete pit depth space. The transition probability function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.

  7. A stochastic model for the financial market with discontinuous prices

    Directory of Open Access Journals (Sweden)

    Leda D. Minkova

    1996-01-01

    Full Text Available This paper models some situations occurring in the financial market. The asset prices evolve according to a stochastic integral equation driven by a Gaussian martingale. A portfolio process is constrained in such a way that the wealth process covers some obligation. A solution to a linear stochastic integral equation is obtained in a class of cadlag stochastic processes.

  8. Stochastic higher spin six vertex model and Macdonald measures

    Science.gov (United States)

    Borodin, Alexei

    2018-02-01

    We prove an identity that relates the q-Laplace transform of the height function of a (higher spin inhomogeneous) stochastic six vertex model in a quadrant on one side and a multiplicative functional of a Macdonald measure on the other. The identity is used to prove the GUE Tracy-Widom asymptotics for two instances of the stochastic six vertex model via asymptotic analysis of the corresponding Schur measures.

  9. Grassmann phase space theory for fermions

    Energy Technology Data Exchange (ETDEWEB)

    Dalton, Bryan J. [Centre for Quantum and Optical Science, Swinburne University of Technology, Melbourne, Victoria, 3122 (Australia); Jeffers, John [Department of Physics, University of Strathclyde, Glasgow, G4 ONG (United Kingdom); Barnett, Stephen M. [School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ (United Kingdom)

    2017-06-15

    A phase space theory for fermions has been developed using Grassmann phase space variables which can be used in numerical calculations for cold Fermi gases and for large fermion numbers. Numerical calculations are feasible because Grassmann stochastic variables at later times are related linearly to such variables at earlier times via c-number stochastic quantities. A Grassmann field version has been developed making large fermion number applications possible. Applications are shown for few mode and field theory cases. (copyright 2016 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  10. Grassmann phase space theory and the Jaynes–Cummings model

    International Nuclear Information System (INIS)

    Dalton, B.J.; Garraway, B.M.; Jeffers, J.; Barnett, S.M.

    2013-01-01

    The Jaynes–Cummings model of a two-level atom in a single mode cavity is of fundamental importance both in quantum optics and in quantum physics generally, involving the interaction of two simple quantum systems—one fermionic system (the TLA), the other bosonic (the cavity mode). Depending on the initial conditions a variety of interesting effects occur, ranging from ongoing oscillations of the atomic population difference at the Rabi frequency when the atom is excited and the cavity is in an n-photon Fock state, to collapses and revivals of these oscillations starting with the atom unexcited and the cavity mode in a coherent state. The observation of revivals for Rydberg atoms in a high-Q microwave cavity is key experimental evidence for quantisation of the EM field. Theoretical treatments of the Jaynes–Cummings model based on expanding the state vector in terms of products of atomic and n-photon states and deriving coupled equations for the amplitudes are a well-known and simple method for determining the effects. In quantum optics however, the behaviour of the bosonic quantum EM field is often treated using phase space methods, where the bosonic mode annihilation and creation operators are represented by c-number phase space variables, with the density operator represented by a distribution function of these variables. Fokker–Planck equations for the distribution function are obtained, and either used directly to determine quantities of experimental interest or used to develop c-number Langevin equations for stochastic versions of the phase space variables from which experimental quantities are obtained as stochastic averages. Phase space methods have also been developed to include atomic systems, with the atomic spin operators being represented by c-number phase space variables, and distribution functions involving these variables and those for any bosonic modes being shown to satisfy Fokker–Planck equations from which c-number Langevin equations are

  11. Scalable inference for stochastic block models

    KAUST Repository

    Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.

    2017-01-01

    Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference

  12. From stochastic phase space evolution to Brownian motion in collective space

    International Nuclear Information System (INIS)

    Benhassine, B.; Farine, M.; Hernandez, E.S.; Idier, D.; Remaud, B.; Sebille, F.

    1993-01-01

    Within the framework of stochastic transport equations in phase space, the dynamics of fluctuations on collective variables in homogeneous fermion systems is studied. The transport coefficients are formally deduced in the relaxation time approximation and a general method to compute dynamically the dispersions of collective observables is proposed as a set of coupled equations. Independently, the general covariance matrix of phase space fluctuations and the dispersion on collective variables at equilibrium are derived. Detailed numerical applications show that dynamics of fluctuations can be extracted from noisy numerical simulations and that the leading parameter for collective fluctuations is the excitation energy whatever is its degree of thermalization. (authors). 16 refs., 12 figs

  13. Assessment model validity document - HYDRASTAR. A stochastic continuum program for groundwater flow

    Energy Technology Data Exchange (ETDEWEB)

    Gylling, B. [Kemakta Konsult AB, Stockholm (Sweden); Eriksson, Lars [Equa Simulation AB, Sundbyberg (Sweden)

    2001-12-01

    The prevailing document addresses validation of the stochastic continuum model HYDRASTAR designed for Monte Carlo simulations of groundwater flow in fractured rocks. Here, validation is defined as a process to demonstrate that a model concept is fit for its purpose. Preferably, the validation is carried out by comparison of model predictions with independent field observations and experimental measurements. In addition, other sources can also be used to confirm that the model concept gives acceptable results. One method is to compare results with the ones achieved using other model concepts for the same set of input data. Another method is to compare model results with analytical solutions. The model concept HYDRASTAR has been used in several studies including performance assessments of hypothetical repositories for spent nuclear fuel. In the performance assessments, the main tasks for HYDRASTAR have been to calculate groundwater travel time distributions, repository flux distributions, path lines and their exit locations. The results have then been used by other model concepts to calculate the near field release and far field transport. The aim and framework for the validation process includes describing the applicability of the model concept for its purpose in order to build confidence in the concept. Preferably, this is made by comparisons of simulation results with the corresponding field experiments or field measurements. Here, two comparisons with experimental results are reported. In both cases the agreement was reasonably fair. In the broader and more general context of the validation process, HYDRASTAR results have been compared with other models and analytical solutions. Commonly, the approximation calculations agree well with the medians of model ensemble results. Additional indications that HYDRASTAR is suitable for its purpose were obtained from the comparisons with results from other model concepts. Several verification studies have been made for

  14. Analysis and development of stochastic multigrid methods in lattice field theory

    International Nuclear Information System (INIS)

    Grabenstein, M.

    1994-01-01

    We study the relation between the dynamical critical behavior and the kinematics of stochastic multigrid algorithms. The scale dependence of acceptance rates for nonlocal Metropolis updates is analyzed with the help of an approximation formula. A quantitative study of the kinematics of multigrid algorithms in several interacting models is performed. We find that for a critical model with Hamiltonian H(Φ) absence of critical slowing down can only be expected if the expansion of (H(Φ+ψ)) in terms of the shift ψ contains no relevant term (mass term). The predictions of this rule was verified in a multigrid Monte Carlo simulation of the Sine Gordon model in two dimensions. Our analysis can serve as a guideline for the development of new algorithms: We propose a new multigrid method for nonabelian lattice gauge theory, the time slice blocking. For SU(2) gauge fields in two dimensions, critical slowing down is almost completely eliminated by this method, in accordance with the theoretical prediction. The generalization of the time slice blocking to SU(2) in four dimensions is investigated analytically and by numerical simulations. Compared to two dimensions, the local disorder in the four dimensional gauge field leads to kinematical problems. (orig.)

  15. Rapidity-density patterns for events in a stochastic-field multiparticle theory

    International Nuclear Information System (INIS)

    Arnold, R.C.

    1976-02-01

    Typical-event rapidity distributions expected at energies of a few TeV are calculated in a stochastic-field multiparticle production theory. Short range rapidity correlations with characteristics of a Van der Waals fluid give rise to ''domain'' patterns in rapidity density, which have the appearance of clusters separated by rapidity gaps

  16. Incorporating the CALPHAD sublattice approach of ordering into the phase-field model with finite interface dissipation

    International Nuclear Information System (INIS)

    Zhang, Lijun; Stratmann, Matthias; Du, Yong; Sundman, Bo; Steinbach, Ingo

    2015-01-01

    A new approach to incorporate the sublattice models in the CALPHAD (CALculation of PHAse Diagram) formalism directly into the phase-field formalism is developed. In binary alloys, the sublattice models can be classified into two types (i.e., “Type I” and “Type II”), depending on whether a direct one-to-one relation between the element site fraction in the CALPHAD database and the phase concentration in the phase-field model exists (Type I), or not (Type II). For “Type II” sublattice models, the specific site fractions, corresponding to a given mole fraction, have to be established via internal relaxation between different sublattices. Internal minimization of sublattice occupancy and solute evolution during microstructure transformation leads, in general, to a solution superior to the separate solution of the individual problems. The present coupling technique is validated for Fe–C and Ni–Al alloys. Finally, the model is extended into multicomponent alloys and applied to simulate the nucleation process of VC monocarbide from austenite matrix in a steel containing vanadium

  17. Phase field

    International Nuclear Information System (INIS)

    Radhakrishnan, B.; Gorti, S.B.; Clarno, K.; Tonks, M.R.

    2015-01-01

    In this work, the phase-field method and its application to microstructure evolution in reactor fuel and clad are discussed. The examples highlight the capability of the phase-field method to capture evolution processes that are influenced by both thermal and elastic stress fields that are caused by microstructural changes in the solid-state. The challenges that need to be overcome before the technique can become predictive and material-specific are discussed. (authors)

  18. Stochastic Parametrisations and Regime Behaviour of Atmospheric Models

    Science.gov (United States)

    Arnold, Hannah; Moroz, Irene; Palmer, Tim

    2013-04-01

    The presence of regimes is a characteristic of non-linear, chaotic systems (Lorenz, 2006). In the atmosphere, regimes emerge as familiar circulation patterns such as the El-Nino Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and Scandinavian Blocking events. In recent years there has been much interest in the problem of identifying and studying atmospheric regimes (Solomon et al, 2007). In particular, how do these regimes respond to an external forcing such as anthropogenic greenhouse gas emissions? The importance of regimes in observed trends over the past 50-100 years indicates that in order to predict anthropogenic climate change, our climate models must be able to represent accurately natural circulation regimes, their statistics and variability. It is well established that representing model uncertainty as well as initial condition uncertainty is important for reliable weather forecasts (Palmer, 2001). In particular, stochastic parametrisation schemes have been shown to improve the skill of weather forecast models (e.g. Berner et al., 2009; Frenkel et al., 2012; Palmer et al., 2009). It is possible that including stochastic physics as a representation of model uncertainty could also be beneficial in climate modelling, enabling the simulator to explore larger regions of the climate attractor including other flow regimes. An alternative representation of model uncertainty is a perturbed parameter scheme, whereby physical parameters in subgrid parametrisation schemes are perturbed about their optimal value. Perturbing parameters gives a greater control over the ensemble than multi-model or multiparametrisation ensembles, and has been used as a representation of model uncertainty in climate prediction (Stainforth et al., 2005; Rougier et al., 2009). We investigate the effect of including representations of model uncertainty on the regime behaviour of a simulator. A simple chaotic model of the atmosphere, the Lorenz '96 system, is used to study

  19. State Space Models and the Kalman-Filter in Stochastic Claims Reserving: Forecasting, Filtering and Smoothing

    Directory of Open Access Journals (Sweden)

    Nataliya Chukhrova

    2017-05-01

    Full Text Available This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving. Most of these state space representations are matrix-based, which complicates their applications. Therefore, to facilitate the implementation of state space models in practice, we present a scalar state space model for cumulative payments, which is an extension of the well-known chain ladder (CL method. The presented model is distribution-free, forms a basis for determining the entire unobservable lower and upper run-off triangles and can easily be applied in practice using the Kalman-filter for prediction, filtering and smoothing of cumulative payments. In addition, the model provides an easy way to find outliers in the data and to determine outlier effects. Finally, an empirical comparison of the scalar state space model, promising prior state space models and some popular stochastic claims reserving methods is performed.

  20. Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise.

    KAUST Repository

    Bressloff, Paul C

    2011-05-03

    We extend the theory of noise-induced phase synchronization to the case of a neural master equation describing the stochastic dynamics of an ensemble of uncoupled neuronal population oscillators with intrinsic and extrinsic noise. The master equation formulation of stochastic neurodynamics represents the state of each population by the number of currently active neurons, and the state transitions are chosen so that deterministic Wilson-Cowan rate equations are recovered in the mean-field limit. We apply phase reduction and averaging methods to a corresponding Langevin approximation of the master equation in order to determine how intrinsic noise disrupts synchronization of the population oscillators driven by a common extrinsic noise source. We illustrate our analysis by considering one of the simplest networks known to generate limit cycle oscillations at the population level, namely, a pair of mutually coupled excitatory (E) and inhibitory (I) subpopulations. We show how the combination of intrinsic independent noise and extrinsic common noise can lead to clustering of the population oscillators due to the multiplicative nature of both noise sources under the Langevin approximation. Finally, we show how a similar analysis can be carried out for another simple population model that exhibits limit cycle oscillations in the deterministic limit, namely, a recurrent excitatory network with synaptic depression; inclusion of synaptic depression into the neural master equation now generates a stochastic hybrid system.