Simulated annealing with constant thermodynamic speed

International Nuclear Information System (INIS)

Salamon, P.; Ruppeiner, G.; Liao, L.; Pedersen, J.

1987-01-01

Arguments are presented to the effect that the optimal annealing schedule for simulated annealing proceeds with constant thermodynamic speed, i.e., with dT/dt = -(v T)/(ε-√C), where T is the temperature, ε- is the relaxation time, C ist the heat capacity, t is the time, and v is the thermodynamic speed. Experimental results consistent with this conjecture are presented from simulated annealing on graph partitioning problems. (orig.)

A note on simulated annealing to computer laboratory scheduling ...

African Journals Online (AJOL)

The concepts, principles and implementation of simulated Annealing as a modem heuristic technique is presented. Simulated Annealing algorithm is used in solving real life problem of Computer Laboratory scheduling in order to maximize the use of scarce and insufficient resources. KEY WORDS: Simulated Annealing ...

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.

Finite-time thermodynamics and simulated annealing

International Nuclear Information System (INIS)

Andresen, B.

1989-01-01

When the general, global optimization technique simulated annealing was introduced by Kirkpatrick et al. (1983), this mathematical algorithm was based on an analogy to the statistical mechanical behavior of real physical systems like spin glasses, hence the name. In the intervening span of years the method has proven exceptionally useful for a great variety of extremely complicated problems, notably NP-problems like the travelling salesman, DNA sequencing, and graph partitioning. Only a few highly optimized heuristic algorithms (e.g. Lin, Kernighan 1973) have outperformed simulated annealing on their respective problems (Johnson et al. 1989). Simulated annealing in its current form relies only on the static quantity 'energy' to describe the system, whereas questions of rate, as in the temperature path (annealing schedule, see below), are left to intuition. We extent the connection to physical systems and take over further components from thermodynamics like ensemble, heat capacity, and relaxation time. Finally we refer to finite-time thermodynamics (Andresen, Salomon, Berry 1984) for a dynamical estimate of the optimal temperature path. (orig.)

A retrodictive stochastic simulation algorithm

International Nuclear Information System (INIS)

Vaughan, T.G.; Drummond, P.D.; Drummond, A.J.

2010-01-01

In this paper we describe a simple method for inferring the initial states of systems evolving stochastically according to master equations, given knowledge of the final states. This is achieved through the use of a retrodictive stochastic simulation algorithm which complements the usual predictive stochastic simulation approach. We demonstrate the utility of this new algorithm by applying it to example problems, including the derivation of likely ancestral states of a gene sequence given a Markovian model of genetic mutation.

Simulated annealing in adaptive optics for imaging the eye retina

International Nuclear Information System (INIS)

Zommer, S.; Adler, J.; Lipson, S. G.; Ribak, E.

2004-01-01

Full Text:Adaptive optics is a method designed to correct deformed images in real time. Once the distorted wavefront is known, a deformable mirror is used to compensate the aberrations and return the wavefront to a plane wave. This study concentrates on methods that omit wave front sensing from the reconstruction process. Such methods use stochastic algorithms to find the extremum of a certain sharpness function, thereby correcting the image without any information on the wavefront. Theoretical work [l] has shown that the optical problem can be mapped onto a model for crystal roughening. The main algorithm applied is simulated annealing. We present a first hardware realization of this algorithm in an adaptive optics system designed to image the retina of the human eye

Intelligent medical image processing by simulated annealing

International Nuclear Information System (INIS)

Ohyama, Nagaaki

1992-01-01

Image processing is being widely used in the medical field and already has become very important, especially when used for image reconstruction purposes. In this paper, it is shown that image processing can be classified into 4 categories; passive, active, intelligent and visual image processing. These 4 classes are explained at first through the use of several examples. The results show that the passive image processing does not give better results than the others. Intelligent image processing, then, is addressed, and the simulated annealing method is introduced. Due to the flexibility of the simulated annealing, formulated intelligence is shown to be easily introduced in an image reconstruction problem. As a practical example, 3D blood vessel reconstruction from a small number of projections, which is insufficient for conventional method to give good reconstruction, is proposed, and computer simulation clearly shows the effectiveness of simulated annealing method. Prior to the conclusion, medical file systems such as IS and C (Image Save and Carry) is pointed out to have potential for formulating knowledge, which is indispensable for intelligent image processing. This paper concludes by summarizing the advantages of simulated annealing. (author)

On lumped models for thermodynamic properties of simulated annealing problems

International Nuclear Information System (INIS)

Andresen, B.; Pedersen, J.M.; Salamon, P.; Hoffmann, K.H.; Mosegaard, K.; Nulton, J.

1987-01-01

The paper describes a new method for the estimation of thermodynamic properties for simulated annealing problems using data obtained during a simulated annealing run. The method works by estimating energy-to-energy transition probabilities and is well adapted to simulations such as simulated annealing, in which the system is never in equilibrium. (orig.)

Variance decomposition in stochastic simulators.

Science.gov (United States)

Le Maître, O P; Knio, O M; Moraes, A

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Science.gov (United States)

Le Maître, O. P.; Knio, O. M.; Moraes, A.

2015-06-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Energy Technology Data Exchange (ETDEWEB)

Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

KAUST Repository

Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro

2015-01-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

On simulated annealing phase transitions in phylogeny reconstruction.

Science.gov (United States)

Strobl, Maximilian A R; Barker, Daniel

2016-08-01

Phylogeny reconstruction with global criteria is NP-complete or NP-hard, hence in general requires a heuristic search. We investigate the powerful, physically inspired, general-purpose heuristic simulated annealing, applied to phylogeny reconstruction. Simulated annealing mimics the physical process of annealing, where a liquid is gently cooled to form a crystal. During the search, periods of elevated specific heat occur, analogous to physical phase transitions. These simulated annealing phase transitions play a crucial role in the outcome of the search. Nevertheless, they have received comparably little attention, for phylogeny or other optimisation problems. We analyse simulated annealing phase transitions during searches for the optimal phylogenetic tree for 34 real-world multiple alignments. In the same way in which melting temperatures differ between materials, we observe distinct specific heat profiles for each input file. We propose this reflects differences in the search landscape and can serve as a measure for problem difficulty and for suitability of the algorithm's parameters. We discuss application in algorithmic optimisation and as a diagnostic to assess parameterisation before computationally costly, large phylogeny reconstructions are launched. Whilst the focus here lies on phylogeny reconstruction under maximum parsimony, it is plausible that our results are more widely applicable to optimisation procedures in science and industry. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

AESS: Accelerated Exact Stochastic Simulation

Science.gov (United States)

Jenkins, David D.; Peterson, Gregory D.

2011-12-01

The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summaryProgram title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution

IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

Directory of Open Access Journals (Sweden)

A. S. Potapov

2014-09-01

Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

Simulated annealing image reconstruction for positron emission tomography

International Nuclear Information System (INIS)

Sundermann, E.; Lemahieu, I.; Desmedt, P.

1994-01-01

In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors)

Simulated annealing image reconstruction for positron emission tomography

Energy Technology Data Exchange (ETDEWEB)

Sundermann, E; Lemahieu, I; Desmedt, P [Department of Electronics and Information Systems, University of Ghent, St. Pietersnieuwstraat 41, B-9000 Ghent, Belgium (Belgium)

1994-12-31

In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors). 11 refs., 2 figs.

Stochastic analysis for finance with simulations

CERN Document Server

Choe, Geon Ho

2016-01-01

This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoret...

Ekofisk chalk: core measurements, stochastic reconstruction, network modeling and simulation

Energy Technology Data Exchange (ETDEWEB)

Talukdar, Saifullah

2002-07-01

This dissertation deals with (1) experimental measurements on petrophysical, reservoir engineering and morphological properties of Ekofisk chalk, (2) numerical simulation of core flood experiments to analyze and improve relative permeability data, (3) stochastic reconstruction of chalk samples from limited morphological information, (4) extraction of pore space parameters from the reconstructed samples, development of network model using pore space information, and computation of petrophysical and reservoir engineering properties from network model, and (5) development of 2D and 3D idealized fractured reservoir models and verification of the applicability of several widely used conventional up scaling techniques in fractured reservoir simulation. Experiments have been conducted on eight Ekofisk chalk samples and porosity, absolute permeability, formation factor, and oil-water relative permeability, capillary pressure and resistivity index are measured at laboratory conditions. Mercury porosimetry data and backscatter scanning electron microscope images have also been acquired for the samples. A numerical simulation technique involving history matching of the production profiles is employed to improve the relative permeability curves and to analyze hysteresis of the Ekofisk chalk samples. The technique was found to be a powerful tool to supplement the uncertainties in experimental measurements. Porosity and correlation statistics obtained from backscatter scanning electron microscope images are used to reconstruct microstructures of chalk and particulate media. The reconstruction technique involves a simulated annealing algorithm, which can be constrained by an arbitrary number of morphological parameters. This flexibility of the algorithm is exploited to successfully reconstruct particulate media and chalk samples using more than one correlation functions. A technique based on conditional simulated annealing has been introduced for exact reproduction of vuggy

Job shop scheduling by simulated annealing

NARCIS (Netherlands)

Laarhoven, van P.J.M.; Aarts, E.H.L.; Lenstra, J.K.

1992-01-01

We describe an approximation algorithm for the problem of finding the minimum makespan in a job shop. The algorithm is based on simulated annealing, a generalization of the well known iterative improvement approach to combinatorial optimization problems. The generalization involves the acceptance of

Reactor controller design using genetic algorithms with simulated annealing

International Nuclear Information System (INIS)

Erkan, K.; Buetuen, E.

2000-01-01

This chapter presents a digital control system for ITU TRIGA Mark-II reactor using genetic algorithms with simulated annealing. The basic principles of genetic algorithms for problem solving are inspired by the mechanism of natural selection. Natural selection is a biological process in which stronger individuals are likely to be winners in a competing environment. Genetic algorithms use a direct analogy of natural evolution. Genetic algorithms are global search techniques for optimisation but they are poor at hill-climbing. Simulated annealing has the ability of probabilistic hill-climbing. Thus, the two techniques are combined here to get a fine-tuned algorithm that yields a faster convergence and a more accurate search by introducing a new mutation operator like simulated annealing or an adaptive cooling schedule. In control system design, there are currently no systematic approaches to choose the controller parameters to obtain the desired performance. The controller parameters are usually determined by test and error with simulation and experimental analysis. Genetic algorithm is used automatically and efficiently searching for a set of controller parameters for better performance. (orig.)

Simulated annealing model of acupuncture

Science.gov (United States)

Shang, Charles; Szu, Harold

2015-05-01

The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person's general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient's age. This is the first biological - physical model of acupuncture which can predict and guide clinical acupuncture research.

Learning FCM by chaotic simulated annealing

International Nuclear Information System (INIS)

Alizadeh, Somayeh; Ghazanfari, Mehdi

2009-01-01

Fuzzy cognitive map (FCM) is a directed graph, which shows the relations between essential components in complex systems. It is a very convenient, simple, and powerful tool, which is used in numerous areas of application. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct FCM by using Chaotic simulated annealing (CSA). The proposed method not only is able to construct FCM graph topology but also is able to extract the weight of the edges from input historical data. The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of Simulated annealing (SA) method.

Fast stochastic algorithm for simulating evolutionary population dynamics

Science.gov (United States)

Tsimring, Lev; Hasty, Jeff; Mather, William

2012-02-01

Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

Thin-film designs by simulated annealing

Science.gov (United States)

Boudet, T.; Chaton, P.; Herault, L.; Gonon, G.; Jouanet, L.; Keller, P.

1996-11-01

With the increasing power of computers, new methods in synthesis of optical multilayer systems have appeared. Among these, the simulated-annealing algorithm has proved its efficiency in several fields of physics. We propose to show its performances in the field of optical multilayer systems through different filter designs.

Stochastic search techniques for post-fault restoration of electrical ...

Indian Academy of Sciences (India)

Three stochastic search techniques have been used to find the optimal sequence of operations required to restore supply in an electrical distribution system on the occurrence of a fault. The three techniques are the genetic algorithm,simulated annealing and the tabu search. The performance of these techniques has been ...

Stochastic Simulation of Process Calculi for Biology

Directory of Open Access Journals (Sweden)

Andrew Phillips

2010-10-01

Full Text Available Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.

Simulated annealing algorithm for reactor in-core design optimizations

International Nuclear Information System (INIS)

Zhong Wenfa; Zhou Quan; Zhong Zhaopeng

2001-01-01

A nuclear reactor must be optimized for in core fuel management to make full use of the fuel, to reduce the operation cost and to flatten the power distribution reasonably. The author presents a simulated annealing algorithm. The optimized objective function and the punishment function were provided for optimizing the reactor physics design. The punishment function was used to practice the simulated annealing algorithm. The practical design of the NHR-200 was calculated. The results show that the K eff can be increased by 2.5% and the power distribution can be flattened

PERBANDINGAN KINERJA ALGORITMA GENETIKA DAN SIMULATED ANNEALING UNTUK MASALAH MULTIPLE OBJECTIVE PADA PENJADWALAN FLOWSHOP

Directory of Open Access Journals (Sweden)

I Gede Agus Widyadana

2002-01-01

Full Text Available The research is focused on comparing Genetics algorithm and Simulated Annealing in the term of performa and processing time. The main purpose is to find out performance both of the algorithm to solve minimizing makespan and total flowtime in a particular flowshop system. Performances of the algorithms are found by simulating problems with variation of jobs and machines combination. The result show the Simulated Annealing is much better than the Genetics up to 90%. The Genetics, however, only had score in processing time, but the trend that plotted suggest that in problems with lots of jobs and lots of machines, the Simulated Annealing will run much faster than the Genetics. Abstract in Bahasa Indonesia : Penelitian ini difokuskan pada pembandingan algoritma Genetika dan Simulated Annealing ditinjau dari aspek performa dan waktu proses. Tujuannya adalah untuk melihat kemampuan dua algoritma tersebut untuk menyelesaikan problem-problem penjadwalan flow shop dengan kriteria minimasi makespan dan total flowtime. Kemampuan kedua algoritma tersebut dilihat dengan melakukan simulasi yang dilakukan pada kombinasi-kombinasi job dan mesin yang berbeda-beda. Hasil simulasi menunjukan algoritma Simulated Annealing lebih unggul dari algoritma Genetika hingga 90%, algoritma Genetika hanya unggul pada waktu proses saja, namun dengan tren waktu proses yang terbentuk, diyakini pada problem dengan kombinasi job dan mesin yang banyak, algoritma Simulated Annealing dapat lebih cepat daripada algoritma Genetika. Kata kunci: Algoritma Genetika, Simulated Annealing, flow shop, makespan, total flowtime.

Loading pattern optimization by multi-objective simulated annealing with screening technique

International Nuclear Information System (INIS)

Tong, K. P.; Hyun, C. L.; Hyung, K. J.; Chang, H. K.

2006-01-01

This paper presents a new multi-objective function which is made up of the main objective term as well as penalty terms related to the constraints. All the terms are represented in the same functional form and the coefficient of each term is normalized so that each term has equal weighting in the subsequent simulated annealing optimization calculations. The screening technique introduced in the previous work is also adopted in order to save computer time in 3-D neutronics evaluation of trial loading patterns. For numerical test of the new multi-objective function in the loading pattern optimization, the optimum loading patterns for the initial and the cycle 7 reload PWR core of Yonggwang Unit 4 are calculated by the simulated annealing algorithm with screening technique. A total of 10 optimum loading patterns are obtained for the initial core through 10 independent simulated annealing optimization runs. For the cycle 7 reload core one optimum loading pattern has been obtained from a single simulated annealing optimization run. More SA optimization runs will be conducted to optimum loading patterns for the cycle 7 reload core and results will be presented in the further work. (authors)

Simulation of Stochastic Loads for Fatigue Experiments

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Brincker, Rune

1989-01-01

process by a Markov process. Two different spectra from two tubular joints in an offshore structure (one narrow banded and one wide banded) are considered in an example. The results show that the simple direct method is quite efficient and results in a simulation speed of about 3000 load cycles per second......A simple direct simulation method for stochastic fatigue-load generation is described in this paper. The simulation method is based on the assumption that only the peaks of the load process significantly affect the fatigue life. The method requires the conditional distribution functions of load...... ranges given the last peak values. Analytical estimates of these distribution functions are presented in the paper and compared with estimates based on a more accurate simulation method. In the more accurate simulation method samples at equidistant times are generated by approximating the stochastic load...

Simulation of Stochastic Loads for Fatigue Experiments

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Brincker, Rune

process by a Markov process. Two different spectra from two tubular joints in an offshore structure (one narrow banded and one wide banded) are considered in an example. The results show that the simple direct method is quite efficient and is results in a simulation speed at about 3000 load cycles per......A simple direct simulation method for stochastic fatigue load generation is described in this paper. The simulation method is based on the assumption that only the peaks of the load process significantly affect the fatigue life. The method requires the conditional distribution functions of load...... ranges given the last peak values. Analytical estimates of these distribution functions are presented in the paper and compared with estimates based on a more accurate simulation method. In the more accurate simulation method samples at equidistant times are generated by approximating the stochastic load...

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...

Simulated annealing approach for solving economic load dispatch ...

African Journals Online (AJOL)

user

thermodynamics to solve economic load dispatch (ELD) problems. ... evolutionary programming algorithm has been successfully applied for solving the ... concept behind the simulated annealing (SA) optimization is discussed in Section 3.

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.

Multiscale Hy3S: Hybrid stochastic simulation for supercomputers

Directory of Open Access Journals (Sweden)

Kaznessis Yiannis N

2006-02-01

Full Text Available Abstract Background Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Results Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users

Simple stochastic simulation.

Science.gov (United States)

Schilstra, Maria J; Martin, Stephen R

2009-01-01

Stochastic simulations may be used to describe changes with time of a reaction system in a way that explicitly accounts for the fact that molecules show a significant degree of randomness in their dynamic behavior. The stochastic approach is almost invariably used when small numbers of molecules or molecular assemblies are involved because this randomness leads to significant deviations from the predictions of the conventional deterministic (or continuous) approach to the simulation of biochemical kinetics. Advances in computational methods over the three decades that have elapsed since the publication of Daniel Gillespie's seminal paper in 1977 (J. Phys. Chem. 81, 2340-2361) have allowed researchers to produce highly sophisticated models of complex biological systems. However, these models are frequently highly specific for the particular application and their description often involves mathematical treatments inaccessible to the nonspecialist. For anyone completely new to the field to apply such techniques in their own work might seem at first sight to be a rather intimidating prospect. However, the fundamental principles underlying the approach are in essence rather simple, and the aim of this article is to provide an entry point to the field for a newcomer. It focuses mainly on these general principles, both kinetic and computational, which tend to be not particularly well covered in specialist literature, and shows that interesting information may even be obtained using very simple operations in a conventional spreadsheet.

Stochastic modeling analysis and simulation

CERN Document Server

Nelson, Barry L

1995-01-01

A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Suitable for advanced undergraduates and graduate-level industrial engineers and management science majors, it proposes modeling systems in terms of their simulation, regardless of whether simulation is employed for analysis. Beginning with a view of the conditions that permit a mathematical-numerical analysis, the text explores Poisson and renewal processes, Markov chains in discrete and continuous time, se

ACTIVITY-BASED COSTING DAN SIMULATED ANNEALING UNTUK PENCARIAN RUTE PADA FLEXIBLE MANUFACTURING SYSTEMS

Directory of Open Access Journals (Sweden)

Gregorius Satia Budhi

2003-01-01

Full Text Available Flexible Manufacturing System (FMS is a manufacturing system that is formed from several Numerical Controlled Machines combine with material handling system, so that different jobs can be worked by different machines sequences. FMS combine the high productivity and flexibility of Transfer Line and Job Shop manufacturing system. In this reasearch, Activity-Based Costing(ABC approach was used as the weight to search the operation route in the proper machine, so that the total production cost can be optimized. The search method that was used in this experiment is Simulated Annealling, a variant form Hill Climbing Search method. An ideal operation time to proses a part was used as the annealling schedule. From the empirical test, it could be proved that the use of ABC approach and Simulated Annealing to search the route (routing process can optimize the Total Production Cost. In the other hand, the use of ideal operation time to process a part as annealing schedule can control the processing time well. Abstract in Bahasa Indonesia : Flexible Manufacturing System (FMS adalah sistem manufaktur yang tersusun dari mesin-mesin Numerical Control (NC yang dikombinasi dengan Sistem Penanganan Material, sehingga job-job berbeda dikerjakan oleh mesin-mesin dengan alur yang berlainan. FMS menggabungkan produktifitas dan fleksibilitas yang tinggi dari Sistem Manufaktur Transfer Line dan Job Shop. Pada riset ini pendekatan Activity-Based Costing (ABC digunakan sebagai bobot / weight dalam pencarian rute operasi pada mesin yang tepat, untuk lebih mengoptimasi biaya produksi secara keseluruhan. Adapun metode Searching yang digunakan adalah Simulated Annealing yang merupakan varian dari metode searching Hill Climbing. Waktu operasi ideal untuk memproses sebuah part digunakan sebagai Annealing Schedulenya. Dari hasil pengujian empiris dapat dibuktikan bahwa penggunaan pendekatan ABC dan Simulated Annealing untuk proses pencarian rute (routing dapat lebih

Experiences with serial and parallel algorithms for channel routing using simulated annealing

Science.gov (United States)

Brouwer, Randall Jay

1988-01-01

Two algorithms for channel routing using simulated annealing are presented. Simulated annealing is an optimization methodology which allows the solution process to back up out of local minima that may be encountered by inappropriate selections. By properly controlling the annealing process, it is very likely that the optimal solution to an NP-complete problem such as channel routing may be found. The algorithm presented proposes very relaxed restrictions on the types of allowable transformations, including overlapping nets. By freeing that restriction and controlling overlap situations with an appropriate cost function, the algorithm becomes very flexible and can be applied to many extensions of channel routing. The selection of the transformation utilizes a number of heuristics, still retaining the pseudorandom nature of simulated annealing. The algorithm was implemented as a serial program for a workstation, and a parallel program designed for a hypercube computer. The details of the serial implementation are presented, including many of the heuristics used and some of the resulting solutions.

Microwave imaging for conducting scatterers by hybrid particle swarm optimization with simulated annealing

International Nuclear Information System (INIS)

Mhamdi, B.; Grayaa, K.; Aguili, T.

2011-01-01

In this paper, a microwave imaging technique for reconstructing the shape of two-dimensional perfectly conducting scatterers by means of a stochastic optimization approach is investigated. Based on the boundary condition and the measured scattered field derived by transverse magnetic illuminations, a set of nonlinear integral equations is obtained and the imaging problem is reformulated in to an optimization problem. A hybrid approximation algorithm, called PSO-SA, is developed in this work to solve the scattering inverse problem. In the hybrid algorithm, particle swarm optimization (PSO) combines global search and local search for finding the optimal results assignment with reasonable time and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of SA. Reconstruction results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

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.

Resorting the NIST undulator using simulated annealing for field error reduction

International Nuclear Information System (INIS)

Denbeaux, Greg; Johnson, Lewis E.; Madey, John M.J.

2000-01-01

We have used a simulated annealing algorithm to sort the samarium cobalt blocks and vanadium permendur poles in the hybrid NIST undulator to optimize the spectrum of the emitted light. While simulated annealing has proven highly effective in sorting of the SmCo blocks in pure REC undulators, the reliance on magnetically 'soft' poles operating near saturation to concentrate the flux in hybrid undulators introduces a pair of additional variables - the permeability and saturation induction of the poles - which limit the utility of the assumption of superposition on which most simulated annealing codes rely. Detailed magnetic measurements clearly demonstrated the failure of the superposition principle due to random variations in the permeability in the 'unsorted' NIST undulator. To deal with the issue, we measured both the magnetization of the REC blocks and the permeability of the NIST's integrated vanadium permendur poles, and implemented a sorting criteria which minimized the pole-to-pole variations in permeability to satisfy the criteria for realization of superposition on a nearest-neighbor basis. Though still imperfect, the computed spectrum of the radiation from the re-sorted and annealed NIST undulator is significantly superior to that of the original, unsorted device

Optimization of pressurized water reactor shuffling by simulated annealing with heuristics

International Nuclear Information System (INIS)

Stevens, J.G.; Smith, K.S.; Rempe, K.R.; Downar, T.J.

1995-01-01

Simulated-annealing optimization of reactor core loading patterns is implemented with support for design heuristics during candidate pattern generation. The SIMAN optimization module uses the advanced nodal method of SIMULATE-3 and the full cross-section detail of CASMO-3 to evaluate accurately the neutronic performance of each candidate, resulting in high-quality patterns. The use of heuristics within simulated annealing is explored. Heuristics improve the consistency of optimization results for both fast- and slow-annealing runs with no penalty from the exclusion of unusual candidates. Thus, the heuristic application of designer judgment during automated pattern generation is shown to be effective. The capability of the SIMAN module to find and evaluate families of loading patterns that satisfy design constraints and have good objective performance within practical run times is demonstrated. The use of automated evaluations of successive cycles to explore multicycle effects of design decisions is discussed

FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

Science.gov (United States)

Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf

2008-08-29

Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new

Very fast simulated re-annealing

OpenAIRE

L. Ingber

1989-01-01

Draft An algorithm is developed to statistically find the best global fit of a nonlinear non-convex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for ‘‘temperature’’ T decreasing exponentially in annealing-time k, T = T0 exp(−ck1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multidimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, ...

Improved operating strategies for uranium extraction: a stochastic simulation

International Nuclear Information System (INIS)

Broekman, B.R.

1986-01-01

Deterministic and stochastic simulations of a Western Transvaal uranium process are used in this research report to determine more profitable uranium plant operating strategies and to gauge the potential financial benefits of automatic process control. The deterministic simulation model was formulated using empirical and phenomenological process models. The model indicated that profitability increases significantly as the uranium leaching strategy becomes harsher. The stochastic simulation models use process variable distributions corresponding to manually and automatically controlled conditions to investigate the economic gains that may be obtained if a change is made from manual to automatic control of two important process variables. These lognormally distributed variables are the pachuca 1 sulphuric acid concentration and the ferric to ferrous ratio. The stochastic simulations show that automatic process control is justifiable in certain cases. Where the leaching strategy is relatively harsh, such as that in operation during January 1986, it is not possible to justify an automatic control system. Automatic control is, however, justifiable if a relatively mild leaching strategy is adopted. The stochastic and deterministic simulations represent two different approaches to uranium process modelling. This study has indicated the necessity for each approach to be applied in the correct context. It is contended that incorrect conclusions may have been drawn by other investigators in South Africa who failed to consider the two approaches separately

Simulation and inference for stochastic processes with YUIMA a comprehensive R framework for SDEs and other stochastic processes

CERN Document Server

Iacus, Stefano M

2018-01-01

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these ...

SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

Science.gov (United States)

Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

2018-03-01

Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

Binary Sparse Phase Retrieval via Simulated Annealing

Directory of Open Access Journals (Sweden)

Wei Peng

2016-01-01

Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

Provably unbounded memory advantage in stochastic simulation using quantum mechanics

Science.gov (United States)

Garner, Andrew J. P.; Liu, Qing; Thompson, Jayne; Vedral, Vlatko; Gu, mile

2017-10-01

Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart.

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Directory of Open Access Journals (Sweden)

Hayder Amer

2016-06-01

Full Text Available Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities.

Science.gov (United States)

Amer, Hayder; Salman, Naveed; Hawes, Matthew; Chaqfeh, Moumena; Mihaylova, Lyudmila; Mayfield, Martin

2016-06-30

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads' length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO₂ emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

Molecular dynamics simulation of annealed ZnO surfaces

Energy Technology Data Exchange (ETDEWEB)

Min, Tjun Kit; Yoon, Tiem Leong [School of Physics, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia); Lim, Thong Leng [Faculty of Engineering and Technology, Multimedia University, Melaka Campus, 75450 Melaka (Malaysia)

2015-04-24

The effect of thermally annealing a slab of wurtzite ZnO, terminated by two surfaces, (0001) (which is oxygen-terminated) and (0001{sup ¯}) (which is Zn-terminated), is investigated via molecular dynamics simulation by using reactive force field (ReaxFF). We found that upon heating beyond a threshold temperature of ∼700 K, surface oxygen atoms begin to sublimate from the (0001) surface. The ratio of oxygen leaving the surface at a given temperature increases as the heating temperature increases. A range of phenomena occurring at the atomic level on the (0001) surface has also been explored, such as formation of oxygen dimers on the surface and evolution of partial charge distribution in the slab during the annealing process. It was found that the partial charge distribution as a function of the depth from the surface undergoes a qualitative change when the annealing temperature is above the threshold temperature.

STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB

KAUST Repository

Klingbeil, G.

2011-02-25

Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user\\'s models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. © The Author 2011. Published by Oxford University Press. All rights reserved.

Provably unbounded memory advantage in stochastic simulation using quantum mechanics

International Nuclear Information System (INIS)

Garner, Andrew J P; Thompson, Jayne; Vedral, Vlatko; Gu, Mile; Liu, Qing

2017-01-01

Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart. (paper)

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

International Nuclear Information System (INIS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-01-01

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

Energy Technology Data Exchange (ETDEWEB)

Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

2016-07-15

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

Ideal versus real: simulated annealing of experimentally derived and geometric platinum nanoparticles

Science.gov (United States)

Ellaby, Tom; Aarons, Jolyon; Varambhia, Aakash; Jones, Lewys; Nellist, Peter; Ozkaya, Dogan; Sarwar, Misbah; Thompsett, David; Skylaris, Chris-Kriton

2018-04-01

Platinum nanoparticles find significant use as catalysts in industrial applications such as fuel cells. Research into their design has focussed heavily on nanoparticle size and shape as they greatly influence activity. Using high throughput, high precision electron microscopy, the structures of commercially available Pt catalysts have been determined, and we have used classical and quantum atomistic simulations to examine and compare them with geometric cuboctahedral and truncated octahedral structures. A simulated annealing procedure was used both to explore the potential energy surface at different temperatures, and also to assess the effect on catalytic activity that annealing would have on nanoparticles with different geometries and sizes. The differences in response to annealing between the real and geometric nanoparticles are discussed in terms of thermal stability, coordination number and the proportion of optimal binding sites on the surface of the nanoparticles. We find that annealing both experimental and geometric nanoparticles results in structures that appear similar in shape and predicted activity, using oxygen adsorption as a measure. Annealing is predicted to increase the catalytic activity in all cases except the truncated octahedra, where it has the opposite effect. As our simulations have been performed with a classical force field, we also assess its suitability to describe the potential energy of such nanoparticles by comparing with large scale density functional theory calculations.

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Directory of Open Access Journals (Sweden)

Luca Marchetti

2017-01-01

Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.

Ranking important nodes in complex networks by simulated annealing

International Nuclear Information System (INIS)

Sun Yu; Yao Pei-Yang; Shen Jian; Zhong Yun; Wan Lu-Jun

2017-01-01

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks. (paper)

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

Science.gov (United States)

Bernal, Javier; Torres-Jimenez, Jose

2015-01-01

SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

New "Tau-Leap" Strategy for Accelerated Stochastic Simulation.

Science.gov (United States)

Ramkrishna, Doraiswami; Shu, Che-Chi; Tran, Vu

2014-12-10

The "Tau-Leap" strategy for stochastic simulations of chemical reaction systems due to Gillespie and co-workers has had considerable impact on various applications. This strategy is reexamined with Chebyshev's inequality for random variables as it provides a rigorous probabilistic basis for a measured τ-leap thus adding significantly to simulation efficiency. It is also shown that existing strategies for simulation times have no probabilistic assurance that they satisfy the τ-leap criterion while the use of Chebyshev's inequality leads to a specified degree of certainty with which the τ-leap criterion is satisfied. This reduces the loss of sample paths which do not comply with the τ-leap criterion. The performance of the present algorithm is assessed, with respect to one discussed by Cao et al. ( J. Chem. Phys. 2006 , 124 , 044109), a second pertaining to binomial leap (Tian and Burrage J. Chem. Phys. 2004 , 121 , 10356; Chatterjee et al. J. Chem. Phys. 2005 , 122 , 024112; Peng et al. J. Chem. Phys. 2007 , 126 , 224109), and a third regarding the midpoint Poisson leap (Peng et al., 2007; Gillespie J. Chem. Phys. 2001 , 115 , 1716). The performance assessment is made by estimating the error in the histogram measured against that obtained with the so-called stochastic simulation algorithm. It is shown that the current algorithm displays notably less histogram error than its predecessor for a fixed computation time and, conversely, less computation time for a fixed accuracy. This computational advantage is an asset in repetitive calculations essential for modeling stochastic systems. The importance of stochastic simulations is derived from diverse areas of application in physical and biological sciences, process systems, and economics, etc. Computational improvements such as those reported herein are therefore of considerable significance.

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.

Improving Simulated Annealing by Replacing Its Variables with Game-Theoretic Utility Maximizers

Science.gov (United States)

Wolpert, David H.; Bandari, Esfandiar; Tumer, Kagan

2001-01-01

The game-theory field of Collective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved as a side-effect. Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting significantly improves simulated annealing for a model of an economic process run over an underlying small-worlds topology. Furthermore, these experiments reveal novel small-worlds phenomena, and highlight the shortcomings of conventional mechanism design in bounded rationality domains.

Monte Carlo simulation of fully Markovian stochastic geometries

International Nuclear Information System (INIS)

Lepage, Thibaut; Delaby, Lucie; Malvagi, Fausto; Mazzolo, Alain

2010-01-01

The interest in resolving the equation of transport in stochastic media has continued to increase these last years. For binary stochastic media it is often assumed that the geometry is Markovian, which is never the case in usual environments. In the present paper, based on rigorous mathematical theorems, we construct fully two-dimensional Markovian stochastic geometries and we study their main properties. In particular, we determine a percolation threshold p c , equal to 0.586 ± 0.0015 for such geometries. Finally, Monte Carlo simulations are performed through these geometries and the results compared to homogeneous geometries. (author)

Cascade annealing: an overview

International Nuclear Information System (INIS)

Doran, D.G.; Schiffgens, J.O.

1976-04-01

Concepts and an overview of radiation displacement damage modeling and annealing kinetics are presented. Short-term annealing methodology is described and results of annealing simulations performed on damage cascades generated using the Marlowe and Cascade programs are included. Observations concerning the inconsistencies and inadequacies of current methods are presented along with simulation of high energy cascades and simulation of longer-term annealing

Simulated annealing to handle energy and ancillary services joint management considering electric vehicles

DEFF Research Database (Denmark)

Sousa, Tiago M; Soares, Tiago; Morais, Hugo

2016-01-01

The massive use of distributed generation and electric vehicles will lead to a more complex management of the power system, requiring new approaches to be used in the optimal resource scheduling field. Electric vehicles with vehicle-to-grid capability can be useful for the aggregator players...... in the mitigation of renewable sources intermittency and in the ancillary services procurement. In this paper, an energy and ancillary services joint management model is proposed. A simulated annealing approach is used to solve the joint management for the following day, considering the minimization...... of the aggregator total operation costs. The case study considers a distribution network with 33-bus, 66 distributed generation and 2000 electric vehicles. The proposed simulated annealing is matched with a deterministic approach allowing an effective and efficient comparison. The simulated annealing presents...

GillesPy: A Python Package for Stochastic Model Building and Simulation

OpenAIRE

Abel, John H.; Drawert, Brian; Hellander, Andreas; Petzold, Linda R.

2016-01-01

GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we descr...

Optimisation of electron beam characteristics by simulated annealing

International Nuclear Information System (INIS)

Ebert, M.A.; University of Adelaide, SA; Hoban, P.W.

1996-01-01

Full text: With the development of technology in the field of treatment beam delivery, the possibility of tailoring radiation beams (via manipulation of the beam's phase space) is foreseeable. This investigation involved evaluating a method for determining the characteristics of pure electron beams which provided dose distributions that best approximated desired distributions. The aim is to determine which degrees of freedom are advantageous and worth pursuing in a clinical setting. A simulated annealing routine was developed to determine optimum electron beam characteristics. A set of beam elements are defined at the surface of a homogeneous water equivalent phantom defining discrete positions and angles of incidence, and electron energies. The optimal weighting of these elements is determined by the (generally approximate) solution to the linear equation, Dw = d, where d represents the dose distribution calculated over the phantom, w the vector of (50 - 2x10 4 ) beam element relative weights, and D a normalised matrix of dose deposition kernels. In the iterative annealing procedure, beam elements are randomly selected and beam weighting distributions are sampled and used to perturb the selected elements. Perturbations are accepted or rejected according to standard simulated annealing criteria. The result (after the algorithm has terminated due to meeting an iteration or optimisation specification) is an approximate solution for the beam weight vector (w) specified by the above equation. This technique has been applied for several sample dose distributions and phase space restrictions. An example is given of the phase space obtained when endeavouring to conform to a rectangular 100% dose region with polyenergetic though normally incident electrons. For regular distributions, intuitive conclusions regarding the benefits of energy/angular manipulation may be made, whereas for complex distributions, variations in intensity over beam elements of varying energy and

Annealing simulation of cascade damage using MARLOWE-DAIQUIRI codes

International Nuclear Information System (INIS)

Muroga, Takeo

1984-01-01

The localization effect of the defects generated by the cascade damage on the properties of solids was studied by using a computer code. The code is based on the two-body collision approximation method and the Monte Carlo method. The MARLOWE and DAIQUIRI codes were partly improved to fit the present calculation of the annealing of cascade damage. The purpose of this study is to investigate the behavior of defects under the simulated reactive and irradiation condition. Calculation was made for alpha iron (BCC), and the threshold energy was set at 40 eV. The temperature dependence of annealing and the growth of a cluster were studied. The overlapping effect of cascade was studied. At first, the extreme case of overlapping was studied, then the practical cases were estimated by interpolation. The state of overlapping of cascade corresponded to the irradiation speed. The interaction between cascade and dislocations was studied, and the calculation of the annealing of primary knock-out atoms (PKA) in alpha iron was performed. At low temperature, the effect of dislocations was large, but the growth of vacancy was not seen. At high temperature, the effect of dislocations was small. The evaluation of the simulation of various ion irradiation and the growth efficiency of defects were performed. (Kato, T.)

Stochastic simulation of biological reactions, and its applications for studying actin polymerization.

Science.gov (United States)

Ichikawa, Kazuhisa; Suzuki, Takashi; Murata, Noboru

2010-11-30

Molecular events in biological cells occur in local subregions, where the molecules tend to be small in number. The cytoskeleton, which is important for both the structural changes of cells and their functions, is also a countable entity because of its long fibrous shape. To simulate the local environment using a computer, stochastic simulations should be run. We herein report a new method of stochastic simulation based on random walk and reaction by the collision of all molecules. The microscopic reaction rate P(r) is calculated from the macroscopic rate constant k. The formula involves only local parameters embedded for each molecule. The results of the stochastic simulations of simple second-order, polymerization, Michaelis-Menten-type and other reactions agreed quite well with those of deterministic simulations when the number of molecules was sufficiently large. An analysis of the theory indicated a relationship between variance and the number of molecules in the system, and results of multiple stochastic simulation runs confirmed this relationship. We simulated Ca²(+) dynamics in a cell by inward flow from a point on the cell surface and the polymerization of G-actin forming F-actin. Our results showed that this theory and method can be used to simulate spatially inhomogeneous events.

Stochastic simulation of biological reactions, and its applications for studying actin polymerization

International Nuclear Information System (INIS)

Ichikawa, Kazuhisa; Suzuki, Takashi; Murata, Noboru

2010-01-01

Molecular events in biological cells occur in local subregions, where the molecules tend to be small in number. The cytoskeleton, which is important for both the structural changes of cells and their functions, is also a countable entity because of its long fibrous shape. To simulate the local environment using a computer, stochastic simulations should be run. We herein report a new method of stochastic simulation based on random walk and reaction by the collision of all molecules. The microscopic reaction rate P r is calculated from the macroscopic rate constant k. The formula involves only local parameters embedded for each molecule. The results of the stochastic simulations of simple second-order, polymerization, Michaelis–Menten-type and other reactions agreed quite well with those of deterministic simulations when the number of molecules was sufficiently large. An analysis of the theory indicated a relationship between variance and the number of molecules in the system, and results of multiple stochastic simulation runs confirmed this relationship. We simulated Ca 2+ dynamics in a cell by inward flow from a point on the cell surface and the polymerization of G-actin forming F-actin. Our results showed that this theory and method can be used to simulate spatially inhomogeneous events

STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB.

Science.gov (United States)

Klingbeil, Guido; Erban, Radek; Giles, Mike; Maini, Philip K

2011-04-15

The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information. klingbeil@maths.ox.ac.uk Supplementary data are available at Bioinformatics online.

Stochastic simulation of off-shore oil terminal systems

International Nuclear Information System (INIS)

Frankel, E.G.; Oberle, J.

1991-01-01

To cope with the problem of uncertainty and conditionality in the planning, design, and operation of offshore oil transshipment terminal systems, a conditional stochastic simulation approach is presented. Examples are shown, using SLAM II, a computer simulation language based on GERT, a conditional stochastic network analysis methodology in which use of resources such as time and money are expressed by the moment generating function of the statistics of the resource requirements. Similarly each activity has an associated conditional probability of being performed and/or of requiring some of the resources. The terminal system is realistically represented by modelling the statistics of arrivals, loading and unloading times, uncertainties in costs and availabilities, etc

Simulated parallel annealing within a neighborhood for optimization of biomechanical systems.

Science.gov (United States)

Higginson, J S; Neptune, R R; Anderson, F C

2005-09-01

Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.

Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications

Science.gov (United States)

Weiss, Charles J.

2017-01-01

An introduction to digital stochastic simulations for modeling a variety of physical and chemical processes is presented. Despite the importance of stochastic simulations in chemistry, the prevalence of turn-key software solutions can impose a layer of abstraction between the user and the underlying approach obscuring the methodology being…

MEDICAL STAFF SCHEDULING USING SIMULATED ANNEALING

Directory of Open Access Journals (Sweden)

Ladislav Rosocha

2015-07-01

Full Text Available Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem.Methodology/Approach: We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time. Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.

Stochastic Simulation of Biomolecular Reaction Networks Using the Biomolecular Network Simulator Software

National Research Council Canada - National Science Library

Frazier, John; Chusak, Yaroslav; Foy, Brent

2008-01-01

.... The software uses either exact or approximate stochastic simulation algorithms for generating Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks...

Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

International Nuclear Information System (INIS)

Aubry, Jean-Francois; Beaulieu, Frederic; Sevigny, Caroline; Beaulieu, Luc; Tremblay, Daniel

2006-01-01

Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented

Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing

NARCIS (Netherlands)

Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.

2006-01-01

The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval

Parallel Stochastic discrete event simulation of calcium dynamics in neuron.

Science.gov (United States)

Ishlam Patoary, Mohammad Nazrul; Tropper, Carl; McDougal, Robert A; Zhongwei, Lin; Lytton, William W

2017-09-26

The intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and calcium concentrations are so low that one extra molecule diffusing in by chance can make a nontrivial difference in its concentration (percentage-wise). These rare events can affect dynamics discretely in such way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level. Our research focuses on the development of a high performance parallel discrete event simulation environment, Neuron Time Warp (NTW), which is intended for use in the parallel simulation of stochastic reaction-diffusion systems such as intra-calcium signaling. NTW is integrated with NEURON, a simulator which is widely used within the neuroscience community. We simulate two models, a calcium buffer and a calcium wave model. The calcium buffer model is employed in order to verify the correctness and performance of NTW by comparing it to a serial deterministic simulation in NEURON. We also derived a discrete event calcium wave model from a deterministic model using the stochastic IP3R structure.

Stochastic simulation of enzyme-catalyzed reactions with disparate timescales.

Science.gov (United States)

Barik, Debashis; Paul, Mark R; Baumann, William T; Cao, Yang; Tyson, John J

2008-10-01

Many physiological characteristics of living cells are regulated by protein interaction networks. Because the total numbers of these protein species can be small, molecular noise can have significant effects on the dynamical properties of a regulatory network. Computing these stochastic effects is made difficult by the large timescale separations typical of protein interactions (e.g., complex formation may occur in fractions of a second, whereas catalytic conversions may take minutes). Exact stochastic simulation may be very inefficient under these circumstances, and methods for speeding up the simulation without sacrificing accuracy have been widely studied. We show that the "total quasi-steady-state approximation" for enzyme-catalyzed reactions provides a useful framework for efficient and accurate stochastic simulations. The method is applied to three examples: a simple enzyme-catalyzed reaction where enzyme and substrate have comparable abundances, a Goldbeter-Koshland switch, where a kinase and phosphatase regulate the phosphorylation state of a common substrate, and coupled Goldbeter-Koshland switches that exhibit bistability. Simulations based on the total quasi-steady-state approximation accurately capture the steady-state probability distributions of all components of these reaction networks. In many respects, the approximation also faithfully reproduces time-dependent aspects of the fluctuations. The method is accurate even under conditions of poor timescale separation.

List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

Directory of Open Access Journals (Sweden)

Shi-hua Zhan

2016-01-01

Full Text Available Simulated annealing (SA algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters’ setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA algorithm to solve traveling salesman problem (TSP. LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.

Selection of views to materialize using simulated annealing algorithms

Science.gov (United States)

Zhou, Lijuan; Liu, Chi; Wang, Hongfeng; Liu, Daixin

2002-03-01

A data warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support or OLAP queries. It is important to select the right view to materialize that answer a given set of queries. The goal is the minimization of the combination of the query evaluation and view maintenance costs. In this paper, we have addressed and designed algorithms for selecting a set of views to be materialized so that the sum of processing a set of queries and maintaining the materialized views is minimized. We develop an approach using simulated annealing algorithms to solve it. First, we explore simulated annealing algorithms to optimize the selection of materialized views. Then we use experiments to demonstrate our approach. The results show that our algorithm works better. We implemented our algorithms and a performance study of the algorithms shows that the proposed algorithm gives an optimal solution.

Simulation of anaerobic digestion processes using stochastic algorithm.

Science.gov (United States)

Palanichamy, Jegathambal; Palani, Sundarambal

2014-01-01

The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'τ' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

Atomic scale simulations of arsenic ion implantation and annealing in silicon

International Nuclear Information System (INIS)

Caturla, M.J.; Diaz de la Rubia, T.; Jaraiz, M.

1995-01-01

We present results of multiple-time-scale simulations of 5, 10 and 15 keV low temperature ion implantation of arsenic on silicon (100), followed by high temperature anneals. The simulations start with a molecular dynamics (MD) calculation of the primary state of damage after 10ps. The results are then coupled to a kinetic Monte Carlo (MC) simulation of bulk defect diffusion and clustering. Dose accumulation is achieved considering that at low temperatures the damage produced in the lattice is stable. After the desired dose is accumulated, the system is annealed at 800 degrees C for several seconds. The results provide information on the evolution for the damage microstructure over macroscopic length and time scales and affords direct comparison to experimental results. We discuss the database of inputs to the MC model and how it affects the diffusion process

GillesPy: A Python Package for Stochastic Model Building and Simulation.

Science.gov (United States)

Abel, John H; Drawert, Brian; Hellander, Andreas; Petzold, Linda R

2016-09-01

GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community.

Simulated annealing algorithm for optimal capital growth

Science.gov (United States)

Luo, Yong; Zhu, Bo; Tang, Yong

2014-08-01

We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.

MONTE CARLO SIMULATION OF MULTIFOCAL STOCHASTIC SCANNING SYSTEM

Directory of Open Access Journals (Sweden)

LIXIN LIU

2014-01-01

Full Text Available Multifocal multiphoton microscopy (MMM has greatly improved the utilization of excitation light and imaging speed due to parallel multiphoton excitation of the samples and simultaneous detection of the signals, which allows it to perform three-dimensional fast fluorescence imaging. Stochastic scanning can provide continuous, uniform and high-speed excitation of the sample, which makes it a suitable scanning scheme for MMM. In this paper, the graphical programming language — LabVIEW is used to achieve stochastic scanning of the two-dimensional galvo scanners by using white noise signals to control the x and y mirrors independently. Moreover, the stochastic scanning process is simulated by using Monte Carlo method. Our results show that MMM can avoid oversampling or subsampling in the scanning area and meet the requirements of uniform sampling by stochastically scanning the individual units of the N × N foci array. Therefore, continuous and uniform scanning in the whole field of view is implemented.

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.

New technique for global solar radiation forecasting by simulated annealing and genetic algorithms using

International Nuclear Information System (INIS)

Tolabi, H.B.; Ayob, S.M.

2014-01-01

In this paper, a novel approach based on simulated annealing algorithm as a meta-heuristic method is implemented in MATLAB software to estimate the monthly average daily global solar radiation on a horizontal surface for six different climate cities of Iran. A search method based on genetic algorithm is applied to accelerate problem solving. Results show that simulated annealing based on genetic algorithm search is a suitable method to find the global solar radiation. (author)

Correction of measured multiplicity distributions by the simulated annealing method

International Nuclear Information System (INIS)

Hafidouni, M.

1993-01-01

Simulated annealing is a method used to solve combinatorial optimization problems. It is used here for the correction of the observed multiplicity distribution from S-Pb collisions at 200 GeV/c per nucleon. (author) 11 refs., 2 figs

Hierarchical Network Design Using Simulated Annealing

DEFF Research Database (Denmark)

Thomadsen, Tommy; Clausen, Jens

2002-01-01

networks are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the network. The problem is solved heuristically using simulated annealing which as a sub......-algorithm uses a construction algorithm to determine edges and route the demand. Performance for different versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to find solutions of reasonable quality in approximately 1 hour for networks with 100 nodes....

Simulated annealing for tensor network states

International Nuclear Information System (INIS)

Iblisdir, S

2014-01-01

Markov chains for probability distributions related to matrix product states and one-dimensional Hamiltonians are introduced. With appropriate ‘inverse temperature’ schedules, these chains can be combined into a simulated annealing scheme for ground states of such Hamiltonians. Numerical experiments suggest that a linear, i.e., fast, schedule is possible in non-trivial cases. A natural extension of these chains to two-dimensional settings is next presented and tested. The obtained results compare well with Euclidean evolution. The proposed Markov chains are easy to implement and are inherently sign problem free (even for fermionic degrees of freedom). (paper)

STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB

KAUST Repository

Klingbeil, G.; Erban, R.; Giles, M.; Maini, P. K.

2011-01-01

Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new

Reconstruction of X-rays spectra of clinical linear accelerators using the generalized simulated annealing method

International Nuclear Information System (INIS)

Manrique, John Peter O.; Costa, Alessandro M.

2016-01-01

The spectral distribution of megavoltage X-rays used in radiotherapy departments is a fundamental quantity from which, in principle, all relevant information required for radiotherapy treatments can be determined. To calculate the dose delivered to the patient who make radiation therapy, are used treatment planning systems (TPS), which make use of convolution and superposition algorithms and which requires prior knowledge of the photon fluence spectrum to perform the calculation of three-dimensional doses and thus ensure better accuracy in the tumor control probabilities preserving the normal tissue complication probabilities low. In this work we have obtained the photon fluence spectrum of X-ray of the SIEMENS ONCOR linear accelerator of 6 MV, using an character-inverse method to the reconstruction of the spectra of photons from transmission curves measured for different thicknesses of aluminum; the method used for reconstruction of the spectra is a stochastic technique known as generalized simulated annealing (GSA), based on the work of quasi-equilibrium statistic of Tsallis. For the validation of the reconstructed spectra we calculated the curve of percentage depth dose (PDD) for energy of 6 MV, using Monte Carlo simulation with Penelope code, and from the PDD then calculate the beam quality index TPR_2_0_/_1_0. (author)

Instantons in Quantum Annealing: Thermally Assisted Tunneling Vs Quantum Monte Carlo Simulations

Science.gov (United States)

Jiang, Zhang; Smelyanskiy, Vadim N.; Boixo, Sergio; Isakov, Sergei V.; Neven, Hartmut; Mazzola, Guglielmo; Troyer, Matthias

2015-01-01

Recent numerical result (arXiv:1512.02206) from Google suggested that the D-Wave quantum annealer may have an asymptotic speed-up than simulated annealing, however, the asymptotic advantage disappears when it is compared to quantum Monte Carlo (a classical algorithm despite its name). We show analytically that the asymptotic scaling of quantum tunneling is exactly the same as the escape rate in quantum Monte Carlo for a class of problems. Thus, the Google result might be explained in our framework. We also found that the transition state in quantum Monte Carlo corresponds to the instanton solution in quantum tunneling problems, which is observed in numerical simulations.

A higher-order numerical framework for stochastic simulation of chemical reaction systems.

KAUST Repository

Székely, Tamás

2012-07-15

BACKGROUND: In this paper, we present a framework for improving the accuracy of fixed-step methods for Monte Carlo simulation of discrete stochastic chemical kinetics. Stochasticity is ubiquitous in many areas of cell biology, for example in gene regulation, biochemical cascades and cell-cell interaction. However most discrete stochastic simulation techniques are slow. We apply Richardson extrapolation to the moments of three fixed-step methods, the Euler, midpoint and θ-trapezoidal τ-leap methods, to demonstrate the power of stochastic extrapolation. The extrapolation framework can increase the order of convergence of any fixed-step discrete stochastic solver and is very easy to implement; the only condition for its use is knowledge of the appropriate terms of the global error expansion of the solver in terms of its stepsize. In practical terms, a higher-order method with a larger stepsize can achieve the same level of accuracy as a lower-order method with a smaller one, potentially reducing the computational time of the system. RESULTS: By obtaining a global error expansion for a general weak first-order method, we prove that extrapolation can increase the weak order of convergence for the moments of the Euler and the midpoint τ-leap methods, from one to two. This is supported by numerical simulations of several chemical systems of biological importance using the Euler, midpoint and θ-trapezoidal τ-leap methods. In almost all cases, extrapolation results in an improvement of accuracy. As in the case of ordinary and stochastic differential equations, extrapolation can be repeated to obtain even higher-order approximations. CONCLUSIONS: Extrapolation is a general framework for increasing the order of accuracy of any fixed-step stochastic solver. This enables the simulation of complicated systems in less time, allowing for more realistic biochemical problems to be solved.

A low-bias simulation scheme for the SABR stochastic volatility model

NARCIS (Netherlands)

B. Chen (Bin); C.W. Oosterlee (Cornelis); J.A.M. van der Weide

2012-01-01

htmlabstractThe Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop an lowbias simulation scheme for the SABR-SV model, which deals efficiently with (undesired)

Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations

Energy Technology Data Exchange (ETDEWEB)

Nemenman, Ilya [Los Alamos National Laboratory; Sinitsyn, Nikolai [Los Alamos National Laboratory; Hengartner, Nick [Los Alamos National Laboratory

2008-01-01

We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.

GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R

Directory of Open Access Journals (Sweden)

Mario Pineda-Krch

2008-02-01

Full Text Available The deterministic dynamics of populations in continuous time are traditionally described using coupled, first-order ordinary differential equations. While this approach is accurate for large systems, it is often inadequate for small systems where key species may be present in small numbers or where key reactions occur at a low rate. The Gillespie stochastic simulation algorithm (SSA is a procedure for generating time-evolution trajectories of finite populations in continuous time and has become the standard algorithm for these types of stochastic models. This article presents a simple-to-use and flexible framework for implementing the SSA using the high-level statistical computing language R and the package GillespieSSA. Using three ecological models as examples (logistic growth, Rosenzweig-MacArthur predator-prey model, and Kermack-McKendrick SIRS metapopulation model, this paper shows how a deterministic model can be formulated as a finite-population stochastic model within the framework of SSA theory and how it can be implemented in R. Simulations of the stochastic models are performed using four different SSA Monte Carlo methods: one exact method (Gillespie's direct method; and three approximate methods (explicit, binomial, and optimized tau-leap methods. Comparison of simulation results confirms that while the time-evolution trajectories obtained from the different SSA methods are indistinguishable, the approximate methods are up to four orders of magnitude faster than the exact methods.

HYDRASTAR - a code for stochastic simulation of groundwater flow

International Nuclear Information System (INIS)

Norman, S.

1992-05-01

The computer code HYDRASTAR was developed as a tool for groundwater flow and transport simulations in the SKB 91 safety analysis project. Its conceptual ideas can be traced back to a report by Shlomo Neuman in 1988, see the reference section. The main idea of the code is the treatment of the rock as a stochastic continuum which separates it from the deterministic methods previously employed by SKB and also from the discrete fracture models. The current report is a comprehensive description of HYDRASTAR including such topics as regularization or upscaling of a hydraulic conductivity field, unconditional and conditional simulation of stochastic processes, numerical solvers for the hydrology and streamline equations and finally some proposals for future developments

An Evaluation of the Use of Simulated Annealing to Optimize Thinning Rates for Single Even-Aged Stands

Directory of Open Access Journals (Sweden)

Kai Moriguchi

2015-01-01

Full Text Available We evaluated the potential of simulated annealing as a reliable method for optimizing thinning rates for single even-aged stands. Four types of yield models were used as benchmark models to examine the algorithm’s versatility. Thinning rate, which was constrained to 0–50% every 5 years at stand ages of 10–45 years, was optimized to maximize the net present value for one fixed rotation term (50 years. The best parameters for the simulated annealing were chosen from 113 patterns, using the mean of the net present value from 39 runs to ensure the best performance. We compared the solutions with those from coarse full enumeration to evaluate the method’s reliability and with 39 runs of random search to evaluate its efficiency. In contrast to random search, the best run of simulated annealing for each of the four yield models resulted in a better solution than coarse full enumeration. However, variations in the objective function for two yield models obtained with simulated annealing were significantly larger than those of random search. In conclusion, simulated annealing with optimized parameters is more efficient for optimizing thinning rates than random search. However, it is necessary to execute multiple runs to obtain reliable solutions.

A parallel simulated annealing algorithm for standard cell placement on a hypercube computer

Science.gov (United States)

Jones, Mark Howard

1987-01-01

A parallel version of a simulated annealing algorithm is presented which is targeted to run on a hypercube computer. A strategy for mapping the cells in a two dimensional area of a chip onto processors in an n-dimensional hypercube is proposed such that both small and large distance moves can be applied. Two types of moves are allowed: cell exchanges and cell displacements. The computation of the cost function in parallel among all the processors in the hypercube is described along with a distributed data structure that needs to be stored in the hypercube to support parallel cost evaluation. A novel tree broadcasting strategy is used extensively in the algorithm for updating cell locations in the parallel environment. Studies on the performance of the algorithm on example industrial circuits show that it is faster and gives better final placement results than the uniprocessor simulated annealing algorithms. An improved uniprocessor algorithm is proposed which is based on the improved results obtained from parallelization of the simulated annealing algorithm.

Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

Directory of Open Access Journals (Sweden)

Gai-Ge Wang

2013-01-01

Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Simulated annealing algorithm for solving chambering student-case assignment problem

Science.gov (United States)

Ghazali, Saadiah; Abdul-Rahman, Syariza

2015-12-01

The problem related to project assignment problem is one of popular practical problem that appear nowadays. The challenge of solving the problem raise whenever the complexity related to preferences, the existence of real-world constraints and problem size increased. This study focuses on solving a chambering student-case assignment problem by using a simulated annealing algorithm where this problem is classified under project assignment problem. The project assignment problem is considered as hard combinatorial optimization problem and solving it using a metaheuristic approach is an advantage because it could return a good solution in a reasonable time. The problem of assigning chambering students to cases has never been addressed in the literature before. For the proposed problem, it is essential for law graduates to peruse in chambers before they are qualified to become legal counselor. Thus, assigning the chambering students to cases is a critically needed especially when involving many preferences. Hence, this study presents a preliminary study of the proposed project assignment problem. The objective of the study is to minimize the total completion time for all students in solving the given cases. This study employed a minimum cost greedy heuristic in order to construct a feasible initial solution. The search then is preceded with a simulated annealing algorithm for further improvement of solution quality. The analysis of the obtained result has shown that the proposed simulated annealing algorithm has greatly improved the solution constructed by the minimum cost greedy heuristic. Hence, this research has demonstrated the advantages of solving project assignment problem by using metaheuristic techniques.

Hybrid framework for the simulation of stochastic chemical kinetics

International Nuclear Information System (INIS)

Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos

2016-01-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Hybrid framework for the simulation of stochastic chemical kinetics

Science.gov (United States)

Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos

2016-12-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the "fast" reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Hybrid framework for the simulation of stochastic chemical kinetics

Energy Technology Data Exchange (ETDEWEB)

Duncan, Andrew, E-mail: a.duncan@imperial.ac.uk [Department of Mathematics, Imperial College, South Kensington Campus, London, SW7 2AZ (United Kingdom); Erban, Radek, E-mail: erban@maths.ox.ac.uk [Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom); Zygalakis, Konstantinos, E-mail: k.zygalakis@ed.ac.uk [School of Mathematics, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh, EH9 3FD (United Kingdom)

2016-12-01

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales

Energy Technology Data Exchange (ETDEWEB)

Xiu, Dongbin [Univ. of Utah, Salt Lake City, UT (United States)

2017-03-03

The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.

Cylinder packing by simulated annealing

Directory of Open Access Journals (Sweden)

M. Helena Correia

2000-12-01

Full Text Available This paper is motivated by the problem of loading identical items of circular base (tubes, rolls, ... into a rectangular base (the pallet. For practical reasons, all the loaded items are considered to have the same height. The resolution of this problem consists in determining the positioning pattern of the circular bases of the items on the rectangular pallet, while maximizing the number of items. This pattern will be repeated for each layer stacked on the pallet. Two algorithms based on the meta-heuristic Simulated Annealing have been developed and implemented. The tuning of these algorithms parameters implied running intensive tests in order to improve its efficiency. The algorithms developed were easily extended to the case of non-identical circles.Este artigo aborda o problema de posicionamento de objetos de base circular (tubos, rolos, ... sobre uma base retangular de maiores dimensões. Por razões práticas, considera-se que todos os objetos a carregar apresentam a mesma altura. A resolução do problema consiste na determinação do padrão de posicionamento das bases circulares dos referidos objetos sobre a base de forma retangular, tendo como objetivo a maximização do número de objetos estritamente posicionados no interior dessa base. Este padrão de posicionamento será repetido em cada uma das camadas a carregar sobre a base retangular. Apresentam-se dois algoritmos para a resolução do problema. Estes algoritmos baseiam-se numa meta-heurística, Simulated Annealling, cuja afinação de parâmetros requereu a execução de testes intensivos com o objetivo de atingir um elevado grau de eficiência no seu desempenho. As características dos algoritmos implementados permitiram que a sua extensão à consideração de círculos com raios diferentes fosse facilmente conseguida.

Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

Directory of Open Access Journals (Sweden)

Fuqiang Lu

2017-01-01

Full Text Available IT outsourcing is an effective way to enhance the core competitiveness for many enterprises. But the schedule risk of IT outsourcing project may cause enormous economic loss to enterprise. In this paper, the Distributed Decision Making (DDM theory and the principal-agent theory are used to build a model for schedule risk management of IT outsourcing project. In addition, a hybrid algorithm combining simulated annealing (SA and genetic algorithm (GA is designed, namely, simulated annealing genetic algorithm (SAGA. The effect of the proposed model on the schedule risk management problem is analyzed in the simulation experiment. Meanwhile, the simulation results of the three algorithms GA, SA, and SAGA show that SAGA is the most superior one to the other two algorithms in terms of stability and convergence. Consequently, this paper provides the scientific quantitative proposal for the decision maker who needs to manage the schedule risk of IT outsourcing project.

Lot Sizing Based on Stochastic Demand and Service Level Constraint

Directory of Open Access Journals (Sweden)

hajar shirneshan

2012-06-01

Full Text Available Considering its application, stochastic lot sizing is a significant subject in production planning. Also the concept of service level is more applicable than shortage cost from managers' viewpoint. In this paper, the stochastic multi period multi item capacitated lot sizing problem has been investigated considering service level constraint. First, the single item model has been developed considering service level and with no capacity constraint and then, it has been solved using dynamic programming algorithm and the optimal solution has been derived. Then the model has been generalized to multi item problem with capacity constraint. The stochastic multi period multi item capacitated lot sizing problem is NP-Hard, hence the model could not be solved by exact optimization approaches. Therefore, simulated annealing method has been applied for solving the problem. Finally, in order to evaluate the efficiency of the model, low level criterion has been used .

The afforestation problem: a heuristic method based on simulated annealing

DEFF Research Database (Denmark)

Vidal, Rene Victor Valqui

1992-01-01

This paper presents the afforestation problem, that is the location and design of new forest compartments to be planted in a given area. This optimization problem is solved by a two-step heuristic method based on simulated annealing. Tests and experiences with this method are also presented....

Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics

Directory of Open Access Journals (Sweden)

Nicolas Wieder

2011-01-01

pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.

Stochastic simulation of PWR vessel integrity for pressurized thermal shock conditions

International Nuclear Information System (INIS)

Jackson, P.S.; Moelling, D.S.

1984-01-01

A stochastic simulation methodology is presented for performing probabilistic analyses of Pressurized Water Reactor vessel integrity. Application of the methodology to vessel-specific integrity analyses is described in the context of Pressurized Thermal Shock (PTS) conditions. A Bayesian method is described for developing vessel-specific models of the density of undetected volumetric flaws from ultrasonic inservice inspection results. Uncertainty limits on the probabilistic results due to sampling errors are determined from the results of the stochastic simulation. An example is provided to illustrate the methodology

INTRODUCCIÓN DE ELEMENTOS DE MEMORIA EN EL MÉTODO SIMULATED ANNEALING PARA RESOLVER PROBLEMAS DE PROGRAMACIÓN MULTIOBJETIVO DE MÁQUINAS PARALELAS INTRODUCTION OF MEMORY ELEMENTS IN SIMULATED ANNEALING METHOD TO SOLVE MULTIOBJECTIVE PARALLEL MACHINE SCHEDULING PROBLEMS

Directory of Open Access Journals (Sweden)

Felipe Baesler

2008-12-01

Full Text Available El presente artículo introduce una variante de la metaheurística simulated annealing, para la resolución de problemas de optimización multiobjetivo. Este enfoque se demonina MultiObjective Simulated Annealing with Random Trajectory Search, MOSARTS. Esta técnica agrega al algoritmo Simulated Annealing elementos de memoria de corto y largo plazo para realizar una búsqueda que permita balancear el esfuerzo entre todos los objetivos involucrados en el problema. Los resultados obtenidos se compararon con otras tres metodologías en un problema real de programación de máquinas paralelas, compuesto por 24 trabajos y 2 máquinas idénticas. Este problema corresponde a un caso de estudio real de la industria regional del aserrío. En los experimentos realizados, MOSARTS se comportó de mejor manera que el resto de la herramientas de comparación, encontrando mejores soluciones en términos de dominancia y dispersión.This paper introduces a variant of the metaheuristic simulated annealing, oriented to solve multiobjective optimization problems. This technique is called MultiObjective Simulated Annealing with Random Trajectory Search (MOSARTS. This technique incorporates short an long term memory concepts to Simulated Annealing in order to balance the search effort among all the objectives involved in the problem. The algorithm was tested against three different techniques on a real life parallel machine scheduling problem, composed of 24 jobs and two identical machines. This problem represents a real life case study of the local sawmill industry. The results showed that MOSARTS behaved much better than the other methods utilized, because found better solutions in terms of dominance and frontier dispersion.

Computing Optimal Stochastic Portfolio Execution Strategies: A Parametric Approach Using Simulations

Science.gov (United States)

Moazeni, Somayeh; Coleman, Thomas F.; Li, Yuying

2010-09-01

Computing optimal stochastic portfolio execution strategies under appropriate risk consideration presents great computational challenge. We investigate a parametric approach for computing optimal stochastic strategies using Monte Carlo simulations. This approach allows reduction in computational complexity by computing coefficients for a parametric representation of a stochastic dynamic strategy based on static optimization. Using this technique, constraints can be similarly handled using appropriate penalty functions. We illustrate the proposed approach to minimize the expected execution cost and Conditional Value-at-Risk (CVaR).

D-leaping: Accelerating stochastic simulation algorithms for reactions with delays

International Nuclear Information System (INIS)

Bayati, Basil; Chatelain, Philippe; Koumoutsakos, Petros

2009-01-01

We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays. The present work extends existing accelerated algorithms by distributing, in a time adaptive fashion, the delayed reactions so as to minimize the computational effort while preserving their accuracy. The accuracy of the present algorithm is assessed by comparing its results to those of the corresponding delay differential equations for a representative biochemical system. In addition, the fluctuations produced from the present algorithm are comparable to those from an exact stochastic simulation with delays. The algorithm is used to simulate biochemical systems that model oscillatory gene expression. The results indicate that the present algorithm is competitive with existing works for several benchmark problems while it is orders of magnitude faster for certain systems of biochemical reactions.

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

Displacement cascades and defect annealing in tungsten, Part II: Object kinetic Monte Carlo simulation of tungsten cascade aging

Energy Technology Data Exchange (ETDEWEB)

Nandipati, Giridhar, E-mail: giridhar.nandipati@pnnl.gov [Pacific Northwest National Laboratory, Richland, WA (United States); Setyawan, Wahyu; Heinisch, Howard L. [Pacific Northwest National Laboratory, Richland, WA (United States); Roche, Kenneth J. [Pacific Northwest National Laboratory, Richland, WA (United States); Department of Physics, University of Washington, Seattle, WA 98195 (United States); Kurtz, Richard J. [Pacific Northwest National Laboratory, Richland, WA (United States); Wirth, Brian D. [University of Tennessee, Knoxville, TN (United States)

2015-07-15

The results of object kinetic Monte Carlo (OKMC) simulations of the annealing of primary cascade damage in bulk tungsten using a comprehensive database of cascades obtained from molecular dynamics (Setyawan et al.) are described as a function of primary knock-on atom (PKA) energy at temperatures of 300, 1025 and 2050 K. An increase in SIA clustering coupled with a decrease in vacancy clustering with increasing temperature, in addition to the disparate mobilities of SIAs versus vacancies, causes an interesting effect of temperature on cascade annealing. The annealing efficiency (the ratio of the number of defects after and before annealing) exhibits an inverse U-shape curve as a function of temperature. The capabilities of the newly developed OKMC code KSOME (kinetic simulations of microstructure evolution) used to carry out these simulations are described.

Displacement cascades and defect annealing in tungsten, Part II: Object kinetic Monte Carlo Simulation of Tungsten Cascade Aging

Energy Technology Data Exchange (ETDEWEB)

Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.; Roche, Kenneth J.; Kurtz, Richard J.; Wirth, Brian D.

2015-07-01

The results of object kinetic Monte Carlo (OKMC) simulations of the annealing of primary cascade damage in bulk tungsten using a comprehensive database of cascades obtained from molecular dynamics (Setyawan et al.) are described as a function of primary knock-on atom (PKA) energy at temperatures of 300, 1025 and 2050 K. An increase in SIA clustering coupled with a decrease in vacancy clustering with increasing temperature, in addition to the disparate mobilities of SIAs versus vacancies, causes an interesting effect of temperature on cascade annealing. The annealing efficiency (the ratio of the number of defects after and before annealing) exhibits an inverse U-shape curve as a function of temperature. The capabilities of the newly developed OKMC code KSOME (kinetic simulations of microstructure evolution) used to carry out these simulations are described.

Ensemble annealing of complex physical systems

OpenAIRE

Habeck, Michael

2015-01-01

Algorithms for simulating complex physical systems or solving difficult optimization problems often resort to an annealing process. Rather than simulating the system at the temperature of interest, an annealing algorithm starts at a temperature that is high enough to ensure ergodicity and gradually decreases it until the destination temperature is reached. This idea is used in popular algorithms such as parallel tempering and simulated annealing. A general problem with annealing methods is th...

Simulation of nuclear plant operation into a stochastic energy production model

International Nuclear Information System (INIS)

Pacheco, R.L.

1983-04-01

A simulation model of nuclear plant operation is developed to fit into a stochastic energy production model. In order to improve the stochastic model used, and also reduce its computational time burdened by the aggregation of the model of nuclear plant operation, a study of tail truncation of the unsupplied demand distribution function has been performed. (E.G.) [pt

Research on neutron noise analysis stochastic simulation method for α calculation

International Nuclear Information System (INIS)

Zhong Bin; Shen Huayun; She Ruogu; Zhu Shengdong; Xiao Gang

2014-01-01

The prompt decay constant α has significant application on the physical design and safety analysis in nuclear facilities. To overcome the difficulty of a value calculation with Monte-Carlo method, and improve the precision, a new method based on the neutron noise analysis technology was presented. This method employs the stochastic simulation and the theory of neutron noise analysis technology. Firstly, the evolution of stochastic neutron was simulated by discrete-events Monte-Carlo method based on the theory of generalized Semi-Markov process, then the neutron noise in detectors was solved from neutron signal. Secondly, the neutron noise analysis methods such as Rossia method, Feynman-α method, zero-probability method, and cross-correlation method were used to calculate a value. All of the parameters used in neutron noise analysis method were calculated based on auto-adaptive arithmetic. The a value from these methods accords with each other, the largest relative deviation is 7.9%, which proves the feasibility of a calculation method based on neutron noise analysis stochastic simulation. (authors)

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.

Stochastic optimization of loading pattern for PWR

International Nuclear Information System (INIS)

Smuc, T.; Pevec, D.

1994-01-01

The application of stochastic optimization methods in solving in-core fuel management problems is restrained by the need for a large number of proposed solutions loading patterns, if a high quality final solution is wanted. Proposed loading patterns have to be evaluated by core neutronics simulator, which can impose unrealistic computer time requirements. A new loading pattern optimization code Monte Carlo Loading Pattern Search has been developed by coupling the simulated annealing optimization algorithm with a fast one-and-a-half dimensional core depletion simulator. The structure of the optimization method provides more efficient performance and allows the user to empty precious experience in the search process, thus reducing the search space size. Hereinafter, we discuss the characteristics of the method and illustrate them on the results obtained by solving the PWR reload problem. (authors). 7 refs., 1 tab., 1 fig

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...

Dimension reduction of Karhunen-Loeve expansion for simulation of stochastic processes

Science.gov (United States)

Liu, Zhangjun; Liu, Zixin; Peng, Yongbo

2017-11-01

Conventional Karhunen-Loeve expansions for simulation of stochastic processes often encounter the challenge of dealing with hundreds of random variables. For breaking through the barrier, a random function embedded Karhunen-Loeve expansion method is proposed in this paper. The updated scheme has a similar form to the conventional Karhunen-Loeve expansion, both involving a summation of a series of deterministic orthonormal basis and uncorrelated random variables. While the difference from the updated scheme lies in the dimension reduction of Karhunen-Loeve expansion through introducing random functions as a conditional constraint upon uncorrelated random variables. The random function is expressed as a single-elementary-random-variable orthogonal function in polynomial format (non-Gaussian variables) or trigonometric format (non-Gaussian and Gaussian variables). For illustrative purposes, the simulation of seismic ground motion is carried out using the updated scheme. Numerical investigations reveal that the Karhunen-Loeve expansion with random functions could gain desirable simulation results in case of a moderate sample number, except the Hermite polynomials and the Laguerre polynomials. It has the sound applicability and efficiency in simulation of stochastic processes. Besides, the updated scheme has the benefit of integrating with probability density evolution method, readily for the stochastic analysis of nonlinear structures.

Stochastic simulations of the tetracycline operon

Science.gov (United States)

2011-01-01

Background The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system. Results Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for Escherichia coli. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts. Conclusions Computer simulations of the tetracycline operon afford augmented insight into the interplay between its molecular

Stochastic simulations of the tetracycline operon

Directory of Open Access Journals (Sweden)

Kaznessis Yiannis N

2011-01-01

Full Text Available Abstract Background The tetracycline operon is a self-regulated system. It is found naturally in bacteria where it confers resistance to antibiotic tetracycline. Because of the performance of the molecular elements of the tetracycline operon, these elements are widely used as parts of synthetic gene networks where the protein production can be efficiently turned on and off in response to the presence or the absence of tetracycline. In this paper, we investigate the dynamics of the tetracycline operon. To this end, we develop a mathematical model guided by experimental findings. Our model consists of biochemical reactions that capture the biomolecular interactions of this intriguing system. Having in mind that small biological systems are subjects to stochasticity, we use a stochastic algorithm to simulate the tetracycline operon behavior. A sensitivity analysis of two critical parameters embodied this system is also performed providing a useful understanding of the function of this system. Results Simulations generate a timeline of biomolecular events that confer resistance to bacteria against tetracycline. We monitor the amounts of intracellular TetR2 and TetA proteins, the two important regulatory and resistance molecules, as a function of intrecellular tetracycline. We find that lack of one of the promoters of the tetracycline operon has no influence on the total behavior of this system inferring that this promoter is not essential for Escherichia coli. Sensitivity analysis with respect to the binding strength of tetracycline to repressor and of repressor to operators suggests that these two parameters play a predominant role in the behavior of the system. The results of the simulations agree well with experimental observations such as tight repression, fast gene expression, induction with tetracycline, and small intracellular TetR2 amounts. Conclusions Computer simulations of the tetracycline operon afford augmented insight into the

Powering stochastic reliability models by discrete event simulation

DEFF Research Database (Denmark)

Kozine, Igor; Wang, Xiaoyun

2012-01-01

it difficult to find a solution to the problem. The power of modern computers and recent developments in discrete-event simulation (DES) software enable to diminish some of the drawbacks of stochastic models. In this paper we describe the insights we have gained based on using both Markov and DES models...

Inverse simulated annealing: Improvements and application to amorphous InSb

OpenAIRE

Los, Jan H.; Gabardi, Silvia; Bernasconi, Marco; Kühne, Thomas D.

2014-01-01

An improved inverse simulated annealing method is presented to determine the structure of complex disordered systems from first principles in agreement with available experimental data or desired predetermined target properties. The effectiveness of this method is demonstrated by revisiting the structure of amorphous InSb. The resulting network is mostly tetrahedral and in excellent agreement with available experimental data.

An adaptive algorithm for simulation of stochastic reaction-diffusion processes

International Nuclear Information System (INIS)

Ferm, Lars; Hellander, Andreas; Loetstedt, Per

2010-01-01

We propose an adaptive hybrid method suitable for stochastic simulation of diffusion dominated reaction-diffusion processes. For such systems, simulation of the diffusion requires the predominant part of the computing time. In order to reduce the computational work, the diffusion in parts of the domain is treated macroscopically, in other parts with the tau-leap method and in the remaining parts with Gillespie's stochastic simulation algorithm (SSA) as implemented in the next subvolume method (NSM). The chemical reactions are handled by SSA everywhere in the computational domain. A trajectory of the process is advanced in time by an operator splitting technique and the timesteps are chosen adaptively. The spatial adaptation is based on estimates of the errors in the tau-leap method and the macroscopic diffusion. The accuracy and efficiency of the method are demonstrated in examples from molecular biology where the domain is discretized by unstructured meshes.

Fast and accurate protein substructure searching with simulated annealing and GPUs

Directory of Open Access Journals (Sweden)

Stivala Alex D

2010-09-01

Full Text Available Abstract Background Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif searching. Results We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU. Conclusions The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.

Reconstruction of bremsstrahlung spectra from attenuation data using generalized simulated annealing

International Nuclear Information System (INIS)

Menin, O.H.; Martinez, A.S.; Costa, A.M.

2016-01-01

A generalized simulated annealing algorithm, combined with a suitable smoothing regularization function is used to solve the inverse problem of X-ray spectrum reconstruction from attenuation data. The approach is to set the initial acceptance and visitation temperatures and to standardize the terms of objective function to automate the algorithm to accommodate different spectra ranges. Experiments with both numerical and measured attenuation data are presented. Results show that the algorithm reconstructs spectra shapes accurately. It should be noted that in this algorithm, the regularization function was formulated to guarantee a smooth spectrum, thus, the presented technique does not apply to X-ray spectrum where characteristic radiation are present. - Highlights: • X-ray spectra reconstruction from attenuation data using generalized simulated annealing. • Algorithm employs a smoothing regularization function, and sets the initial acceptance and visitation temperatures. • Algorithmic is automated by standardizing the terms of the objective function. • Algorithm is compared with classical methods.

A constrained approach to multiscale stochastic simulation of chemically reacting systems

KAUST Repository

Cotter, Simon L.

2011-01-01

Stochastic simulation of coupled chemical reactions is often computationally intensive, especially if a chemical system contains reactions occurring on different time scales. In this paper, we introduce a multiscale methodology suitable to address this problem, assuming that the evolution of the slow species in the system is well approximated by a Langevin process. It is based on the conditional stochastic simulation algorithm (CSSA) which samples from the conditional distribution of the suitably defined fast variables, given values for the slow variables. In the constrained multiscale algorithm (CMA) a single realization of the CSSA is then used for each value of the slow variable to approximate the effective drift and diffusion terms, in a similar manner to the constrained mean-force computations in other applications such as molecular dynamics. We then show how using the ensuing Fokker-Planck equation approximation, we can in turn approximate average switching times in stochastic chemical systems. © 2011 American Institute of Physics.

New heating schedule in hydrogen annealing furnace based on process simulation for less energy consumption

International Nuclear Information System (INIS)

Saboonchi, Ahmad; Hassanpour, Saeid; Abbasi, Shahram

2008-01-01

Cold rolled steel coils are annealed in batch furnaces to obtain desirable mechanical properties. Annealing operations involve heating and cooling cycles which take long due to high weight of the coils under annealing. To reduce annealing time, a simulation code was developed that is capable of evaluating more effective schedules for annealing coils during the heating process. This code is additionally capable of accurate determination of furnace turn-off time for different coil weights and charge dimensions. After studying many heating schedules and considering heat transfer mechanism in the annealing furnace, a new schedule with the most advantages was selected as the new operation conditions in the hydrogen annealing plant. The performance of all the furnaces were adjusted to the new heating schedule after experiments had been carried out to ensure the accuracy of the code and the fitness of the new operation condition. Comparison of similar yield of cold rolled coils over two months revealed that specific energy consumption of furnaces under the new heating schedule decreased by 11%, heating cycle time by 16%, and the hydrogen consumption by 14%

New heating schedule in hydrogen annealing furnace based on process simulation for less energy consumption

Energy Technology Data Exchange (ETDEWEB)

Saboonchi, Ahmad [Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 84154 (Iran); Hassanpour, Saeid [Rayan Tahlil Sepahan Co., Isfahan Science and Technology Town, Isfahan 84155 (Iran); Abbasi, Shahram [R and D Department, Mobarakeh Steel Complex, Isfahan (Iran)

2008-11-15

Cold rolled steel coils are annealed in batch furnaces to obtain desirable mechanical properties. Annealing operations involve heating and cooling cycles which take long due to high weight of the coils under annealing. To reduce annealing time, a simulation code was developed that is capable of evaluating more effective schedules for annealing coils during the heating process. This code is additionally capable of accurate determination of furnace turn-off time for different coil weights and charge dimensions. After studying many heating schedules and considering heat transfer mechanism in the annealing furnace, a new schedule with the most advantages was selected as the new operation conditions in the hydrogen annealing plant. The performance of all the furnaces were adjusted to the new heating schedule after experiments had been carried out to ensure the accuracy of the code and the fitness of the new operation condition. Comparison of similar yield of cold rolled coils over two months revealed that specific energy consumption of furnaces under the new heating schedule decreased by 11%, heating cycle time by 16%, and the hydrogen consumption by 14%. (author)

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

Directory of Open Access Journals (Sweden)

Rui Zhang

2013-04-01

Full Text Available A decomposition-based optimization algorithm is proposed for solving large job shop scheduling problems with the objective of minimizing the maximum lateness. First, we use the constraint propagation theory to derive the orientation of a portion of disjunctive arcs. Then we use a simulated annealing algorithm to find a decomposition policy which satisfies the maximum number of oriented disjunctive arcs. Subsequently, each subproblem (corresponding to a subset of operations as determined by the decomposition policy is successively solved with a simulated annealing algorithm, which leads to a feasible solution to the original job shop scheduling problem. Computational experiments are carried out for adapted benchmark problems, and the results show the proposed algorithm is effective and efficient in terms of solution quality and time performance.

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

Science.gov (United States)

Gao, Xiaohui; Liu, Yongguang

2018-01-01

There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm's fast convergence and simulated annealing algorithm's jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.

Software Tools for Stochastic Simulations of Turbulence

Science.gov (United States)

2015-08-28

40] R. D. Richtmyer. Taylor instability in shock acceleration of compressible fluids. Comm. pure Appl. Math , 13(297-319), 1960. 76 [41] R. Samulyak, J...Research Triangle Park, NC 27709-2211 Pure sciences, Applied sciences, Front tracking, Large eddy simulations, Mesh convergence, Stochastic convergence, Weak...Illustration of a component grid with a front crossing solution stencil. Cells in the pure yellow and pure blue regions are assigned different components

Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB.

Science.gov (United States)

Ullah, M; Schmidt, H; Cho, K H; Wolkenhauer, O

2006-03-01

The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) with probabilities as variables. This is to generate counts of molecules for chemical species as realisations of random variables drawn from the probability distribution described by the CMEs. Although there are numerous tools available, many of them free, the modelling and simulation environment MATLAB is widely used in the physical and engineering sciences. We describe a collection of MATLAB functions to construct and solve ODEs for deterministic simulation and to implement realisations of CMEs for stochastic simulation using advanced MATLAB coding (Release 14). The program was successfully applied to pathway models from the literature for both cases. The results were compared to implementations using alternative tools for dynamic modelling and simulation of biochemical networks. The aim is to provide a concise set of MATLAB functions that encourage the experimentation with systems biology models. All the script files are available from www.sbi.uni-rostock.de/ publications_matlab-paper.html.

Analysis of Trivium by a Simulated Annealing variant

DEFF Research Database (Denmark)

Borghoff, Julia; Knudsen, Lars Ramkilde; Matusiewicz, Krystian

2010-01-01

This paper proposes a new method of solving certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations....... A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is provided. As an example, we investigate equation systems induced by the problem of recovering the internal state of the stream cipher Trivium. We propose an improved variant of the simulated annealing method...

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...

Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization

Directory of Open Access Journals (Sweden)

Xuefeng Yan

2013-01-01

Full Text Available The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.

Optimization of Multiple Traveling Salesman Problem Based on Simulated Annealing Genetic Algorithm

Directory of Open Access Journals (Sweden)

Xu Mingji

2017-01-01

Full Text Available It is very effective to solve the multi variable optimization problem by using hierarchical genetic algorithm. This thesis analyzes both advantages and disadvantages of hierarchical genetic algorithm and puts forward an improved simulated annealing genetic algorithm. The new algorithm is applied to solve the multiple traveling salesman problem, which can improve the performance of the solution. First, it improves the design of chromosomes hierarchical structure in terms of redundant hierarchical algorithm, and it suggests a suffix design of chromosomes; Second, concerning to some premature problems of genetic algorithm, it proposes a self-identify crossover operator and mutation; Third, when it comes to the problem of weak ability of local search of genetic algorithm, it stretches the fitness by mixing genetic algorithm with simulated annealing algorithm. Forth, it emulates the problems of N traveling salesmen and M cities so as to verify its feasibility. The simulation and calculation shows that this improved algorithm can be quickly converged to a best global solution, which means the algorithm is encouraging in practical uses.

Comparative analysis of cogeneration power plants optimization based on stochastic method using superstructure and process simulator

Energy Technology Data Exchange (ETDEWEB)

Araujo, Leonardo Rodrigues de [Instituto Federal do Espirito Santo, Vitoria, ES (Brazil)], E-mail: leoaraujo@ifes.edu.br; Donatelli, Joao Luiz Marcon [Universidade Federal do Espirito Santo (UFES), Vitoria, ES (Brazil)], E-mail: joaoluiz@npd.ufes.br; Silva, Edmar Alino da Cruz [Instituto Tecnologico de Aeronautica (ITA/CTA), Sao Jose dos Campos, SP (Brazil); Azevedo, Joao Luiz F. [Instituto de Aeronautica e Espaco (CTA/IAE/ALA), Sao Jose dos Campos, SP (Brazil)

2010-07-01

Thermal systems are essential in facilities such as thermoelectric plants, cogeneration plants, refrigeration systems and air conditioning, among others, in which much of the energy consumed by humanity is processed. In a world with finite natural sources of fuels and growing energy demand, issues related with thermal system design, such as cost estimative, design complexity, environmental protection and optimization are becoming increasingly important. Therefore the need to understand the mechanisms that degrade energy, improve energy sources use, reduce environmental impacts and also reduce project, operation and maintenance costs. In recent years, a consistent development of procedures and techniques for computational design of thermal systems has occurred. In this context, the fundamental objective of this study is a performance comparative analysis of structural and parametric optimization of a cogeneration system using stochastic methods: genetic algorithm and simulated annealing. This research work uses a superstructure, modelled in a process simulator, IPSEpro of SimTech, in which the appropriate design case studied options are included. Accordingly, the cogeneration system optimal configuration is determined as a consequence of the optimization process, restricted within the configuration options included in the superstructure. The optimization routines are written in MsExcel Visual Basic, in order to work perfectly coupled to the simulator process. At the end of the optimization process, the system optimal configuration, given the characteristics of each specific problem, should be defined. (author)

Stochastic Simulation Using @ Risk for Dairy Business Investment Decisions

Science.gov (United States)

A dynamic, stochastic, mechanistic simulation model of a dairy business was developed to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting fram...

PENJADWALAN FLOWSHOP DENGAN MENGGUNAKAN SIMULATED ANNEALING

Directory of Open Access Journals (Sweden)

Muhammad Firdaus

2015-04-01

Full Text Available This article apply a machine scheduling technique, named Simulate Annealing (SA to schedule 8 jobs and 5 machines to minimize makespan. A flowshop production flow is chosen as a case study to collect data and attempted to reduce jobs’ makespan. This article also does a sensitivity analysis to explore the implication of the changes of SA parameters as temperature. The results shows that the completion time of the jobs uses SA algoritm can decrease the completion time of the jobs, about 5 hours lower than the existing method. Moreover, total idle time of the machines is also reduced by 2.18 per cent using SA technique. Based on the sensitivity analysis, it indicates that there is a significant relationship between the changes of temperatures and makespan and computation time.

The two-regime method for optimizing stochastic reaction-diffusion simulations

KAUST Repository

Flegg, M. B.; Chapman, S. J.; Erban, R.

2011-01-01

Spatial organization and noise play an important role in molecular systems biology. In recent years, a number of software packages have been developed for stochastic spatio-temporal simulation, ranging from detailed molecular-based approaches

MOSES: A Matlab-based open-source stochastic epidemic simulator.

Science.gov (United States)

Varol, Huseyin Atakan

2016-08-01

This paper presents an open-source stochastic epidemic simulator. Discrete Time Markov Chain based simulator is implemented in Matlab. The simulator capable of simulating SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model can be reduced to simpler models by setting some of the parameters (transition probabilities) to zero. Similarly, it can be extended to more complicated models by editing the source code. It is designed to be used for testing different control algorithms to contain epidemics. The simulator is also designed to be compatible with a network based epidemic simulator and can be used in the network based scheme for the simulation of a node. Simulations show the capability of reproducing different epidemic model behaviors successfully in a computationally efficient manner.

Stochastic simulation of karst conduit networks

Science.gov (United States)

Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Xu, Chaoshui; Durán-Valsero, Juan José

2012-01-01

Karst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., the network of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and fractures. This heterogeneity strongly influences the hydraulic behavior of the karst and it must be reproduced in any realistic numerical model of the karst system that is used as input to flow and transport modeling. However, the directly observed karst conduits are only a small part of the complete karst conduit system and knowledge of the complete conduit geometry and topology remains spatially limited and uncertain. Thus, there is a special interest in the stochastic simulation of networks of conduits that can be combined with fracture and rock porosity models to provide a realistic numerical model of the karst system. Furthermore, the simulated model may be of interest per se and other uses could be envisaged. The purpose of this paper is to present an efficient method for conditional and non-conditional stochastic simulation of karst conduit networks. The method comprises two stages: generation of conduit geometry and generation of topology. The approach adopted is a combination of a resampling method for generating conduit geometries from templates and a modified diffusion-limited aggregation method for generating the network topology. The authors show that the 3D karst conduit networks generated by the proposed method are statistically similar to observed karst conduit networks or to a hypothesized network model. The statistical similarity is in the sense of reproducing the tortuosity index of conduits, the fractal dimension of the network, the direction rose of directions, the Z-histogram and Ripley's K-function of the bifurcation points (which differs from a random allocation of those bifurcation points). The proposed method (1) is very flexible, (2) incorporates any experimental data (conditioning information) and (3) can easily be modified when

Stochastic simulation using @Risk for dairy business investment decisions

NARCIS (Netherlands)

Bewley, J.D.; Boehlje, M.D.; Gray, A.W.; Hogeveen, H.; Kenyon, S.J.; Eicher, S.D.; Schutz, M.M.

2010-01-01

Purpose – The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2016-07-07

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

KAUST Repository

Kuwahara, Hiroyuki

2011-01-01

Gene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.

Analytical vs. Simulation Solution Techniques for Pulse Problems in Non-linear Stochastic Dynamics

DEFF Research Database (Denmark)

Iwankiewicz, R.; Nielsen, Søren R. K.

Advantages and disadvantages of available analytical and simulation techniques for pulse problems in non-linear stochastic dynamics are discussed. First, random pulse problems, both those which do and do not lead to Markov theory, are presented. Next, the analytical and analytically-numerical tec......Advantages and disadvantages of available analytical and simulation techniques for pulse problems in non-linear stochastic dynamics are discussed. First, random pulse problems, both those which do and do not lead to Markov theory, are presented. Next, the analytical and analytically...

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

International Nuclear Information System (INIS)

Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif

2015-01-01

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system

Solving the patient zero inverse problem by using generalized simulated annealing

Science.gov (United States)

Menin, Olavo H.; Bauch, Chris T.

2018-01-01

Identifying patient zero - the initially infected source of a given outbreak - is an important step in epidemiological investigations of both existing and emerging infectious diseases. Here, the use of the Generalized Simulated Annealing algorithm (GSA) to solve the inverse problem of finding the source of an outbreak is studied. The classical disease natural histories susceptible-infected (SI), susceptible-infected-susceptible (SIS), susceptible-infected-recovered (SIR) and susceptible-infected-recovered-susceptible (SIRS) in a regular lattice are addressed. Both the position of patient zero and its time of infection are considered unknown. The algorithm performance with respect to the generalization parameter q˜v and the fraction ρ of infected nodes for whom infection was ascertained is assessed. Numerical experiments show the algorithm is able to retrieve the epidemic source with good accuracy, even when ρ is small, but present no evidence to support that GSA performs better than its classical version. Our results suggest that simulated annealing could be a helpful tool for identifying patient zero in an outbreak where not all cases can be ascertained.

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Science.gov (United States)

Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif

2015-02-01

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Energy Technology Data Exchange (ETDEWEB)

Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com [Centre of Preparatory and General Studies, TATI University College, 24000 Kemaman, Terengganu, Malaysia and Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Yusof, Fadhilah, E-mail: fadhilahy@utm.my [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Daud, Zalina Mohd, E-mail: zalina@ic.utm.my [UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia); Yusop, Zulkifli, E-mail: zulyusop@utm.my [Institute of Environmental and Water Resource Management (IPASA), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Kasno, Mohammad Afif, E-mail: mafifkasno@gmail.com [Malaysia - Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia)

2015-02-03

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

Simulating the production of free defects in irradiated metals

International Nuclear Information System (INIS)

Heinisch, H.L.

1995-01-01

Under cascade-producing irradiation by high energy neutrons or charged particles, only a small fraction of the initially displaced atoms contribute to the population of free defects that are available to migrate throughout the metal and cause microstructural changes. Although, in principle, computer simulations of free defect production could best be done using molecular dynamics, in practice, the wide ranges of time and distance scales involved can be done only by a combination of atomistic models that employ various levels of approximation. An atomic-scale, multi-model approach has been developed that combines molecular dynamics, binary collision models and stochastic annealing simulation. The annealing simulation is utilized in calibrating binary collision simulations to the results of molecular dynamics calculations, as well as to model the subsequent migration of the defects on more macroscopic time and size scales. The annealing simulation and the method of calibrating the multi-model approach are discussed, and the results of simulations of cascades in copper are presented. The temperature dependence of free defect production following simulated annealing of isolated cascades in copper shows a differential in the fractions of free vacancies and interstitial defects escaping from the cascade above stage V. This differential, a consequence of the direct formation of interstitial clusters in cascades and the relative thermal stability of vacancy and interstitial clusters during subsequent annealing, is the basis for the production bias mechanism of void swelling. (orig.)

The two-regime method for optimizing stochastic reaction-diffusion simulations

KAUST Repository

Flegg, M. B.

2011-10-19

Spatial organization and noise play an important role in molecular systems biology. In recent years, a number of software packages have been developed for stochastic spatio-temporal simulation, ranging from detailed molecular-based approaches to less detailed compartment-based simulations. Compartment-based approaches yield quick and accurate mesoscopic results, but lack the level of detail that is characteristic of the computationally intensive molecular-based models. Often microscopic detail is only required in a small region (e.g. close to the cell membrane). Currently, the best way to achieve microscopic detail is to use a resource-intensive simulation over the whole domain. We develop the two-regime method (TRM) in which a molecular-based algorithm is used where desired and a compartment-based approach is used elsewhere. We present easy-to-implement coupling conditions which ensure that the TRM results have the same accuracy as a detailed molecular-based model in the whole simulation domain. Therefore, the TRM combines strengths of previously developed stochastic reaction-diffusion software to efficiently explore the behaviour of biological models. Illustrative examples and the mathematical justification of the TRM are also presented.

Differential evolution-simulated annealing for multiple sequence alignment

Science.gov (United States)

Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.

2017-10-01

Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

Fast stochastic simulation of biochemical reaction systems by alternative formulations of the chemical Langevin equation

KAUST Repository

Mélykúti, Bence; Burrage, Kevin; Zygalakis, Konstantinos C.

2010-01-01

The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when

Intelligent simulated annealing algorithm applied to the optimization of the main magnet for magnetic resonance imaging machine; Algoritmo simulated annealing inteligente aplicado a la optimizacion del iman principal de una maquina de resonancia magnetica de imagenes

Energy Technology Data Exchange (ETDEWEB)

Sanchez Lopez, Hector [Universidad de Oriente, Santiago de Cuba (Cuba). Centro de Biofisica Medica]. E-mail: hsanchez@cbm.uo.edu.cu

2001-08-01

This work describes an alternative algorithm of Simulated Annealing applied to the design of the main magnet for a Magnetic Resonance Imaging machine. The algorithm uses a probabilistic radial base neuronal network to classify the possible solutions, before the objective function evaluation. This procedure allows reducing up to 50% the number of iterations required by simulated annealing to achieve the global maximum, when compared with the SA algorithm. The algorithm was applied to design a 0.1050 Tesla four coil resistive magnet, which produces a magnetic field 2.13 times more uniform than the solution given by SA. (author)

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist.

Directory of Open Access Journals (Sweden)

Brian Drawert

2016-12-01

Full Text Available We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

Lattice Boltzmann simulation of flow and heat transfer in random porous media constructed by simulated annealing algorithm

International Nuclear Information System (INIS)

Liu, Minghua; Shi, Yong; Yan, Jiashu; Yan, Yuying

2017-01-01

Highlights: • A numerical capability combining the lattice Boltzmann method with simulated annealing algorithm is developed. • Digitized representations of random porous media are constructed using limited but meaningful statistical descriptors. • Pore-scale flow and heat transfer information in random porous media is obtained by the lattice Boltzmann simulation. • The effective properties at the representative elementary volume scale are well specified using appropriate upscale averaging. - Abstract: In this article, the lattice Boltzmann (LB) method for transport phenomena is combined with the simulated annealing (SA) algorithm for digitized porous-medium construction to study flow and heat transfer in random porous media. Importantly, in contrast to previous studies which simplify porous media as arrays of regularly shaped objects or effective pore networks, the LB + SA method in this article can model statistically meaningful random porous structures in irregular morphology, and simulate pore-scale transport processes inside them. Pore-scale isothermal flow and heat conduction in a set of constructed random porous media characterized by statistical descriptors were then simulated through use of the LB + SA method. The corresponding averages over the computational volumes and the related effective transport properties were also computed based on these pore scale numerical results. Good agreement between the numerical results and theoretical predictions or experimental data on the representative elementary volume scale was found. The numerical simulations in this article demonstrate combination of the LB method with the SA algorithm is a viable and powerful numerical strategy for simulating transport phenomena in random porous media in complex geometries.

Stochastic sensitivity analysis and Langevin simulation for neural network learning

International Nuclear Information System (INIS)

Koda, Masato

1997-01-01

A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method

Simulation of quantum dynamics based on the quantum stochastic differential equation.

Science.gov (United States)

Li, Ming

2013-01-01

The quantum stochastic differential equation derived from the Lindblad form quantum master equation is investigated. The general formulation in terms of environment operators representing the quantum state diffusion is given. The numerical simulation algorithm of stochastic process of direct photodetection of a driven two-level system for the predictions of the dynamical behavior is proposed. The effectiveness and superiority of the algorithm are verified by the performance analysis of the accuracy and the computational cost in comparison with the classical Runge-Kutta algorithm.

Simulation of Stochastic Processes by Coupled ODE-PDE

Science.gov (United States)

Zak, Michail

2008-01-01

A document discusses the emergence of randomness in solutions of coupled, fully deterministic ODE-PDE (ordinary differential equations-partial differential equations) due to failure of the Lipschitz condition as a new phenomenon. It is possible to exploit the special properties of ordinary differential equations (represented by an arbitrarily chosen, dynamical system) coupled with the corresponding Liouville equations (used to describe the evolution of initial uncertainties in terms of joint probability distribution) in order to simulate stochastic processes with the proscribed probability distributions. The important advantage of the proposed approach is that the simulation does not require a random-number generator.

The time dependent propensity function for acceleration of spatial stochastic simulation of reaction–diffusion systems

International Nuclear Information System (INIS)

Fu, Jin; Wu, Sheng; Li, Hong; Petzold, Linda R.

2014-01-01

The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy

Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2015-01-01

even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.

Parallel simulated annealing algorithms for cell placement on hypercube multiprocessors

Science.gov (United States)

Banerjee, Prithviraj; Jones, Mark Howard; Sargent, Jeff S.

1990-01-01

Two parallel algorithms for standard cell placement using simulated annealing are developed to run on distributed-memory message-passing hypercube multiprocessors. The cells can be mapped in a two-dimensional area of a chip onto processors in an n-dimensional hypercube in two ways, such that both small and large cell exchange and displacement moves can be applied. The computation of the cost function in parallel among all the processors in the hypercube is described, along with a distributed data structure that needs to be stored in the hypercube to support the parallel cost evaluation. A novel tree broadcasting strategy is used extensively for updating cell locations in the parallel environment. A dynamic parallel annealing schedule estimates the errors due to interacting parallel moves and adapts the rate of synchronization automatically. Two novel approaches in controlling error in parallel algorithms are described: heuristic cell coloring and adaptive sequence control.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

Energy Technology Data Exchange (ETDEWEB)

Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

International Nuclear Information System (INIS)

Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao

2014-01-01

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm

Phase diagram of 2D Hubbard model by simulated annealing mean field approximation

International Nuclear Information System (INIS)

Kato, Masaru; Kitagaki, Takashi

1991-01-01

In order to investigate the stable magnetic structure of the Hubbard model on a square lattice, we utilize the dynamical simulated annealing method which proposed by R. Car and M. Parrinello. Results of simulations on a 10 x 10 lattice system with 80 electrons under assumption of collinear magnetic structure that the most stable state is incommensurate spin density wave state with periodic domain wall. (orig.)

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.

A constrained approach to multiscale stochastic simulation of chemically reacting systems

KAUST Repository

Cotter, Simon L.; Zygalakis, Konstantinos C.; Kevrekidis, Ioannis G.; Erban, Radek

2011-01-01

Stochastic simulation of coupled chemical reactions is often computationally intensive, especially if a chemical system contains reactions occurring on different time scales. In this paper, we introduce a multiscale methodology suitable to address

Simulation of multivariate stationary stochastic processes using dimension-reduction representation methods

Science.gov (United States)

Liu, Zhangjun; Liu, Zenghui; Peng, Yongbo

2018-03-01

In view of the Fourier-Stieltjes integral formula of multivariate stationary stochastic processes, a unified formulation accommodating spectral representation method (SRM) and proper orthogonal decomposition (POD) is deduced. By introducing random functions as constraints correlating the orthogonal random variables involved in the unified formulation, the dimension-reduction spectral representation method (DR-SRM) and the dimension-reduction proper orthogonal decomposition (DR-POD) are addressed. The proposed schemes are capable of representing the multivariate stationary stochastic process with a few elementary random variables, bypassing the challenges of high-dimensional random variables inherent in the conventional Monte Carlo methods. In order to accelerate the numerical simulation, the technique of Fast Fourier Transform (FFT) is integrated with the proposed schemes. For illustrative purposes, the simulation of horizontal wind velocity field along the deck of a large-span bridge is proceeded using the proposed methods containing 2 and 3 elementary random variables. Numerical simulation reveals the usefulness of the dimension-reduction representation methods.

Stochastic simulation of ecohydrological interactions between vegetation and groundwater

Science.gov (United States)

Dwelle, M. C.; Ivanov, V. Y.; Sargsyan, K.

2017-12-01

The complex interactions between groundwater and vegetation in the Amazon rainforest may yield vital ecophysiological interactions in specific landscape niches such as buffering plant water stress during dry season or suppression of water uptake due to anoxic conditions. Representation of such processes is greatly impacted by both external and internal sources of uncertainty: inaccurate data and subjective choice of model representation. The models that can simulate these processes are complex and computationally expensive, and therefore make it difficult to address uncertainty using traditional methods. We use the ecohydrologic model tRIBS+VEGGIE and a novel uncertainty quantification framework applied to the ZF2 watershed near Manaus, Brazil. We showcase the capability of this framework for stochastic simulation of vegetation-hydrology dynamics. This framework is useful for simulation with internal and external stochasticity, but this work will focus on internal variability of groundwater depth distribution and model parameterizations. We demonstrate the capability of this framework to make inferences on uncertain states of groundwater depth from limited in situ data, and how the realizations of these inferences affect the ecohydrological interactions between groundwater dynamics and vegetation function. We place an emphasis on the probabilistic representation of quantities of interest and how this impacts the understanding and interpretation of the dynamics at the groundwater-vegetation interface.

Analysing initial attack on wildland fires using stochastic simulation.

Science.gov (United States)

Jeremy S. Fried; J. Keith Gilless; James. Spero

2006-01-01

Stochastic simulation models of initial attack on wildland fire can be designed to reflect the complexity of the environmental, administrative, and institutional context in which wildland fire protection agencies operate, but such complexity may come at the cost of a considerable investment in data acquisition and management. This cost may be well justified when it...

Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics.

Science.gov (United States)

Chen, Minghan; Li, Fei; Wang, Shuo; Cao, Young

2017-03-14

Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

Directory of Open Access Journals (Sweden)

Hailong Wang

2018-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

Stochastic four-way coupling of gas-solid flows for Large Eddy Simulations

Science.gov (United States)

Curran, Thomas; Denner, Fabian; van Wachem, Berend

2017-11-01

The interaction of solid particles with turbulence has for long been a topic of interest for predicting the behavior of industrially relevant flows. For the turbulent fluid phase, Large Eddy Simulation (LES) methods are widely used for their low computational cost, leaving only the sub-grid scales (SGS) of turbulence to be modelled. Although LES has seen great success in predicting the behavior of turbulent single-phase flows, the development of LES for turbulent gas-solid flows is still in its infancy. This contribution aims at constructing a model to describe the four-way coupling of particles in an LES framework, by considering the role particles play in the transport of turbulent kinetic energy across the scales. Firstly, a stochastic model reconstructing the sub-grid velocities for the particle tracking is presented. Secondly, to solve particle-particle interaction, most models involve a deterministic treatment of the collisions. We finally introduce a stochastic model for estimating the collision probability. All results are validated against fully resolved DNS-DPS simulations. The final goal of this contribution is to propose a global stochastic method adapted to two-phase LES simulation where the number of particles considered can be significantly increased. Financial support from PetroBras is gratefully acknowledged.

Simulating biological processes: stochastic physics from whole cells to colonies

Science.gov (United States)

Earnest, Tyler M.; Cole, John A.; Luthey-Schulten, Zaida

2018-05-01

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a ‘minimal cell’. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Simulation, hardware implementation and control of a multilevel inverter with simulated annealing algorithm

Directory of Open Access Journals (Sweden)

Fayçal Chabni

2017-09-01

Full Text Available Harmonic pollution is a very common issue in the field of power electronics, Harmonics can cause multiple problems for power converters and electrical loads alike, this paper introduces a modulation method called selective harmonic elimination pulse width modulation (SHEPWM, this method allows the elimination of a specific order of harmonics and also control the amplitude of the fundamental component of the output voltage. In this work SHEPWM strategy is applied to a five level cascade inverter. The objective of this study is to demonstrate the total control provided by the SHEPWM strategy over any rank of harmonics using the simulated annealing optimization algorithm and also control the amplitude of the fundamental component at any desired value. Simulation and experimental results are presented in this work.

Adaptive Finite Element Method Assisted by Stochastic Simulation of Chemical Systems

KAUST Repository

Cotter, Simon L.; Vejchodský , Tomá š; Erban, Radek

2013-01-01

Stochastic models of chemical systems are often analyzed by solving the corresponding Fokker-Planck equation, which is a drift-diffusion partial differential equation for the probability distribution function. Efficient numerical solution of the Fokker-Planck equation requires adaptive mesh refinements. In this paper, we present a mesh refinement approach which makes use of a stochastic simulation of the underlying chemical system. By observing the stochastic trajectory for a relatively short amount of time, the areas of the state space with nonnegligible probability density are identified. By refining the finite element mesh in these areas, and coarsening elsewhere, a suitable mesh is constructed and used for the computation of the stationary probability density. Numerical examples demonstrate that the presented method is competitive with existing a posteriori methods. © 2013 Society for Industrial and Applied Mathematics.

Quantum Annealing and Quantum Fluctuation Effect in Frustrated Ising Systems

OpenAIRE

Tanaka, Shu; Tamura, Ryo

2012-01-01

Quantum annealing method has been widely attracted attention in statistical physics and information science since it is expected to be a powerful method to obtain the best solution of optimization problem as well as simulated annealing. The quantum annealing method was incubated in quantum statistical physics. This is an alternative method of the simulated annealing which is well-adopted for many optimization problems. In the simulated annealing, we obtain a solution of optimization problem b...

Stochastic simulation of regional groundwater flow in Beishan area

International Nuclear Information System (INIS)

Dong Yanhui; Li Guomin

2010-01-01

Because of the hydrogeological complexity, traditional thinking of aquifer characteristics is not appropriate for groundwater system in Beishan area. Uncertainty analysis of groundwater models is needed to examine the hydrologic effects of spatial heterogeneity. In this study, fast Fourier transform spectral method (FFTS) was used to generate the random horizontal permeability parameters. Depth decay and vertical anisotropy of hydraulic conductivity were included to build random permeability models. Based on high-performance computers, hundreds of groundwater flow models were simulated. Through stochastic simulations, the effect of heterogeneity to groundwater flow pattern was analyzed. (authors)

Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics.

Science.gov (United States)

Lin, Yen Ting; Chylek, Lily A; Lemons, Nathan W; Hlavacek, William S

2018-06-21

The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.

Long-time analytic approximation of large stochastic oscillators: Simulation, analysis and inference.

Directory of Open Access Journals (Sweden)

Giorgos Minas

2017-07-01

Full Text Available In order to analyse large complex stochastic dynamical models such as those studied in systems biology there is currently a great need for both analytical tools and also algorithms for accurate and fast simulation and estimation. We present a new stochastic approximation of biological oscillators that addresses these needs. Our method, called phase-corrected LNA (pcLNA overcomes the main limitations of the standard Linear Noise Approximation (LNA to remain uniformly accurate for long times, still maintaining the speed and analytically tractability of the LNA. As part of this, we develop analytical expressions for key probability distributions and associated quantities, such as the Fisher Information Matrix and Kullback-Leibler divergence and we introduce a new approach to system-global sensitivity analysis. We also present algorithms for statistical inference and for long-term simulation of oscillating systems that are shown to be as accurate but much faster than leaping algorithms and algorithms for integration of diffusion equations. Stochastic versions of published models of the circadian clock and NF-κB system are used to illustrate our results.

Simulated annealing and joint manufacturing batch-sizing

Directory of Open Access Journals (Sweden)

Sarker Ruhul

2003-01-01

Full Text Available We address an important problem of a manufacturing system. The system procures raw materials from outside suppliers in a lot and processes them to produce finished goods. It proposes an ordering policy for raw materials to meet the requirements of a production facility. In return, this facility has to deliver finished products demanded by external buyers at fixed time intervals. First, a general cost model is developed considering both raw materials and finished products. Then this model is used to develop a simulated annealing approach to determining an optimal ordering policy for procurement of raw materials and also for the manufacturing batch size to minimize the total cost for meeting customer demands in time. The solutions obtained were compared with those of traditional approaches. Numerical examples are presented. .

Simulated annealing method for electronic circuits design: adaptation and comparison with other optimization methods

International Nuclear Information System (INIS)

Berthiau, G.

1995-10-01

The circuit design problem consists in determining acceptable parameter values (resistors, capacitors, transistors geometries ...) which allow the circuit to meet various user given operational criteria (DC consumption, AC bandwidth, transient times ...). This task is equivalent to a multidimensional and/or multi objective optimization problem: n-variables functions have to be minimized in an hyper-rectangular domain ; equality constraints can be eventually specified. A similar problem consists in fitting component models. In this way, the optimization variables are the model parameters and one aims at minimizing a cost function built on the error between the model response and the data measured on the component. The chosen optimization method for this kind of problem is the simulated annealing method. This method, provided by the combinatorial optimization domain, has been adapted and compared with other global optimization methods for the continuous variables problems. An efficient strategy of variables discretization and a set of complementary stopping criteria have been proposed. The different parameters of the method have been adjusted with analytical functions of which minima are known, classically used in the literature. Our simulated annealing algorithm has been coupled with an open electrical simulator SPICE-PAC of which the modular structure allows the chaining of simulations required by the circuit optimization process. We proposed, for high-dimensional problems, a partitioning technique which ensures proportionality between CPU-time and variables number. To compare our method with others, we have adapted three other methods coming from combinatorial optimization domain - the threshold method, a genetic algorithm and the Tabu search method - The tests have been performed on the same set of test functions and the results allow a first comparison between these methods applied to continuous optimization variables. Finally, our simulated annealing program

Optimization of the energy production for the Baghdara hydropower plant in Afghanistan using simulated annealing; Optimierung der Energieerzeugung fuer das Wasserkraftwerk Baghdara in Afghanistan mit simulated annealing

Energy Technology Data Exchange (ETDEWEB)

Ayros, E.; Hildebrandt, H.; Peissner, K. [Fichtner GmbH und Co. KG, Stuttgart (Germany). Wasserbau und Wasserkraftwerke; Bardossy, A. [Stuttgart Univ. (Germany). Inst. fuer Wasserbau

2008-07-01

Simulated Annealing (SA) is an optimization method analogous to the thermodynamic method and is a new alternative for optimising the energy production of hydropower systems with storage capabilities. The SA-Algorithm is presented here and it was applied for the maximization of the energy production of the Baghdara hydropower plant in Afghanistan. The results were also compared with a non-linear optimization method NLP. (orig.)

Database of Nucleon-Nucleon Scattering Cross Sections by Stochastic Simulation, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — A database of nucleon-nucleon elastic differential and total cross sections will be generated by stochastic simulation of the quantum Liouville equation in the...

Use of simulated annealing in standardization and optimization of the acerola wine production

Directory of Open Access Journals (Sweden)

Sheyla dos Santos Almeida

2014-06-01

Full Text Available In this study, seven wine samples were prepared varying the amount of pulp of acerola fruits and the sugar content using the simulated annealing technique to obtain the optimal sensory qualities and cost for the wine produced. S. cerevisiae yeast was used in the fermentation process and the sensory attributes were evaluated using a hedonic scale. Acerola wines were classified as sweet, with 11°GL of alcohol concentration and with aroma, taste, and color characteristics of the acerola fruit. The simulated annealing experiments showed that the best conditions were found at mass ratio between 1/7.5-1/6 and total soluble solids between 28.6-29.0 °Brix, from which the sensory acceptance scores of 6.9, 6.8, and 8.8 were obtained for color, aroma, and flavor, respectively, with a production cost 43-45% lower than the cost of traditional wines commercialized in Brazil.

Intelligent simulated annealing algorithm applied to the optimization of the main magnet for magnetic resonance imaging machine

International Nuclear Information System (INIS)

Sanchez Lopez, Hector

2001-01-01

This work describes an alternative algorithm of Simulated Annealing applied to the design of the main magnet for a Magnetic Resonance Imaging machine. The algorithm uses a probabilistic radial base neuronal network to classify the possible solutions, before the objective function evaluation. This procedure allows reducing up to 50% the number of iterations required by simulated annealing to achieve the global maximum, when compared with the SA algorithm. The algorithm was applied to design a 0.1050 Tesla four coil resistive magnet, which produces a magnetic field 2.13 times more uniform than the solution given by SA. (author)

Kinetic Monte Carlo simulation of nanostructural evolution under post-irradiation annealing in dilute FeMnNi

Energy Technology Data Exchange (ETDEWEB)

Chiapetto, M. [SCK-CEN, Nuclear Materials Science Institute, Mol (Belgium); Unite Materiaux et Transformations (UMET), UMR 8207, Universite de Lille 1, ENSCL, Villeneuve d' Ascq (France); Becquart, C.S. [Unite Materiaux et Transformations (UMET), UMR 8207, Universite de Lille 1, ENSCL, Villeneuve d' Ascq (France); Laboratoire commun EDF-CNRS, Etude et Modelisation des Microstructures pour le Vieillissement des Materiaux (EM2VM) (France); Domain, C. [EDF R and D, Departement Materiaux et Mecanique des Composants, Les Renardieres, Moret sur Loing (France); Laboratoire commun EDF-CNRS, Etude et Modelisation des Microstructures pour le Vieillissement des Materiaux (EM2VM) (France); Malerba, L. [SCK-CEN, Nuclear Materials Science Institute, Mol (Belgium)

2015-01-01

Post-irradiation annealing experiments are often used to obtain clearer information on the nature of defects produced by irradiation. However, their interpretation is not always straightforward without the support of physical models. We apply here a physically-based set of parameters for object kinetic Monte Carlo (OKMC) simulations of the nanostructural evolution of FeMnNi alloys under irradiation to the simulation of their post-irradiation isochronal annealing, from 290 to 600 C. The model adopts a ''grey alloy'' scheme, i.e. the solute atoms are not introduced explicitly, only their effect on the properties of point-defect clusters is. Namely, it is assumed that both vacancy and SIA clusters are significantly slowed down by the solutes. The slowing down increases with size until the clusters become immobile. Specifically, the slowing down of SIA clusters by Mn and Ni can be justified in terms of the interaction between these atoms and crowdions in Fe. The results of the model compare quantitatively well with post-irradiation isochronal annealing experimental data, providing clear insight into the mechanisms that determine the disappearance or re-arrangement of defects as functions of annealing time and temperature. (copyright 2015 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

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.

Spatially explicit and stochastic simulation of forest landscape fire disturbance and succession

Science.gov (United States)

Hong S. He; David J. Mladenoff

1999-01-01

Understanding disturbance and recovery of forest landscapes is a challenge because of complex interactions over a range of temporal and spatial scales. Landscape simulation models offer an approach to studying such systems at broad scales. Fire can be simulated spatially using mechanistic or stochastic approaches. We describe the fire module in a spatially explicit,...

spsann - optimization of sample patterns using spatial simulated annealing

Science.gov (United States)

Samuel-Rosa, Alessandro; Heuvelink, Gerard; Vasques, Gustavo; Anjos, Lúcia

2015-04-01

There are many algorithms and computer programs to optimize sample patterns, some private and others publicly available. A few have only been presented in scientific articles and text books. This dispersion and somewhat poor availability is holds back to their wider adoption and further development. We introduce spsann, a new R-package for the optimization of sample patterns using spatial simulated annealing. R is the most popular environment for data processing and analysis. Spatial simulated annealing is a well known method with widespread use to solve optimization problems in the soil and geo-sciences. This is mainly due to its robustness against local optima and easiness of implementation. spsann offers many optimizing criteria for sampling for variogram estimation (number of points or point-pairs per lag distance class - PPL), trend estimation (association/correlation and marginal distribution of the covariates - ACDC), and spatial interpolation (mean squared shortest distance - MSSD). spsann also includes the mean or maximum universal kriging variance (MUKV) as an optimizing criterion, which is used when the model of spatial variation is known. PPL, ACDC and MSSD were combined (PAN) for sampling when we are ignorant about the model of spatial variation. spsann solves this multi-objective optimization problem scaling the objective function values using their maximum absolute value or the mean value computed over 1000 random samples. Scaled values are aggregated using the weighted sum method. A graphical display allows to follow how the sample pattern is being perturbed during the optimization, as well as the evolution of its energy state. It is possible to start perturbing many points and exponentially reduce the number of perturbed points. The maximum perturbation distance reduces linearly with the number of iterations. The acceptance probability also reduces exponentially with the number of iterations. R is memory hungry and spatial simulated annealing is a

The Parameters Optimization of MCR-WPT System Based on the Improved Genetic Simulated Annealing Algorithm

Directory of Open Access Journals (Sweden)

Sheng Lu

2015-01-01

Full Text Available To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT, this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.

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.

Direct comparison of quantum and simulated annealing on a fully connected Ising ferromagnet

Science.gov (United States)

Wauters, Matteo M.; Fazio, Rosario; Nishimori, Hidetoshi; Santoro, Giuseppe E.

2017-08-01

We compare the performance of quantum annealing (QA, through Schrödinger dynamics) and simulated annealing (SA, through a classical master equation) on the p -spin infinite range ferromagnetic Ising model, by slowly driving the system across its equilibrium, quantum or classical, phase transition. When the phase transition is second order (p =2 , the familiar two-spin Ising interaction) SA shows a remarkable exponential speed-up over QA. For a first-order phase transition (p ≥3 , i.e., with multispin Ising interactions), in contrast, the classical annealing dynamics appears to remain stuck in the disordered phase, while we have clear evidence that QA shows a residual energy which decreases towards zero when the total annealing time τ increases, albeit in a rather slow (logarithmic) fashion. This is one of the rare examples where a limited quantum speedup, a speedup by QA over SA, has been shown to exist by direct solutions of the Schrödinger and master equations in combination with a nonequilibrium Landau-Zener analysis. We also analyze the imaginary-time QA dynamics of the model, finding a 1 /τ2 behavior for all finite values of p , as predicted by the adiabatic theorem of quantum mechanics. The Grover-search limit p (odd )=∞ is also discussed.

Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions

KAUST Repository

Klingbeil, Guido; Erban, Radek; Giles, Mike; Maini, Philip K.

2012-01-01

We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimize data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximizes parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie [14]. In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system's size. © 2006 IEEE.

Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions

KAUST Repository

Klingbeil, Guido

2012-02-01

We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimize data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximizes parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie [14]. In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system\\'s size. © 2006 IEEE.

Improve earthquake hypocenter using adaptive simulated annealing inversion in regional tectonic, volcano tectonic, and geothermal observation

Energy Technology Data Exchange (ETDEWEB)

Ry, Rexha Verdhora, E-mail: rexha.vry@gmail.com [Master Program of Geophysical Engineering, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132 (Indonesia); Nugraha, Andri Dian, E-mail: nugraha@gf.itb.ac.id [Global Geophysical Research Group, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132 (Indonesia)

2015-04-24

Observation of earthquakes is routinely used widely in tectonic activity observation, and also in local scale such as volcano tectonic and geothermal activity observation. It is necessary for determining the location of precise hypocenter which the process involves finding a hypocenter location that has minimum error between the observed and the calculated travel times. When solving this nonlinear inverse problem, simulated annealing inversion method can be applied to such global optimization problems, which the convergence of its solution is independent of the initial model. In this study, we developed own program codeby applying adaptive simulated annealing inversion in Matlab environment. We applied this method to determine earthquake hypocenter using several data cases which are regional tectonic, volcano tectonic, and geothermal field. The travel times were calculated using ray tracing shooting method. We then compared its results with the results using Geiger’s method to analyze its reliability. Our results show hypocenter location has smaller RMS error compared to the Geiger’s result that can be statistically associated with better solution. The hypocenter of earthquakes also well correlated with geological structure in the study area. Werecommend using adaptive simulated annealing inversion to relocate hypocenter location in purpose to get precise and accurate earthquake location.

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

Science.gov (United States)

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Application of simulated annealing for simultaneous retrieval of particle size distribution and refractive index

International Nuclear Information System (INIS)

Ma, Lin; Kranendonk, Laura; Cai, Weiwei; Zhao, Yan; Baba, Justin S.

2009-01-01

This paper describes the application of the simulated annealing technique for the simultaneous retrieval of particle size distribution and refractive index based on polarization modulated scattering (PMS) measurements. The PMS technique is a well-established method to measure multiple elements of the Mueller scattering matrix. However, the inference of the scatterers properties (e.g., the size distribution function and refractive index) from such measurements involves solving an ill-conditioned inverse problem. In this paper, a new inversion technique was demonstrated to infer particle properties from PMS measurements. The new technique formulated the inverse problem into a minimization problem, which is then solved by the simulated annealing technique. Both numerical and experimental investigation on the new inversion technique was presented in the paper. The results obtained demonstrated the robustness and reliability of the new algorithm, and supported its expanded applications in scientific and technological areas involving particulates/aerosols.

StochKit2: software for discrete stochastic simulation of biochemical systems with events.

Science.gov (United States)

Sanft, Kevin R; Wu, Sheng; Roh, Min; Fu, Jin; Lim, Rone Kwei; Petzold, Linda R

2011-09-01

StochKit2 is the first major upgrade of the popular StochKit stochastic simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie's stochastic simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/. petzold@engineering.ucsb.edu.

Simulation of the diffusion of implanted impurities in silicon structures at the rapid thermal annealing

International Nuclear Information System (INIS)

Komarov, F.F.; Komarov, A.F.; Mironov, A.M.; Makarevich, Yu.V.; Miskevich, S.A.; Zayats, G.M.

2011-01-01

Physical and mathematical models and numerical simulation of the diffusion of implanted impurities during rapid thermal treatment of silicon structures are discussed. The calculation results correspond to the experimental results with a sufficient accuracy. A simulation software system has been developed that is integrated into ATHENA simulation system developed by Silvaco Inc. This program can simulate processes of the low-energy implantation of B, BF 2 , P, As, Sb, C ions into the silicon structures and subsequent rapid thermal annealing. (authors)

Stochastic and simulation models of maritime intercept operations capabilities

OpenAIRE

Sato, Hiroyuki

2005-01-01

The research formulates and exercises stochastic and simulation models to assess the Maritime Intercept Operations (MIO) capabilities. The models focus on the surveillance operations of the Maritime Patrol Aircraft (MPA). The analysis using the models estimates the probability with which a terrorist vessel (Red) is detected, correctly classified, and escorted for intensive investigation and neutralization before it leaves an area of interest (AOI). The difficulty of obtaining adequate int...

INCLUDING RISK IN ECONOMIC FEASIBILITY ANALYSIS:A STOCHASTIC SIMULATION MODEL FOR BLUEBERRY INVESTMENT DECISIONS IN CHILE

Directory of Open Access Journals (Sweden)

GERMÁN LOBOS

2015-12-01

Full Text Available ABSTRACT The traditional method of net present value (NPV to analyze the economic profitability of an investment (based on a deterministic approach does not adequately represent the implicit risk associated with different but correlated input variables. Using a stochastic simulation approach for evaluating the profitability of blueberry (Vaccinium corymbosum L. production in Chile, the objective of this study is to illustrate the complexity of including risk in economic feasibility analysis when the project is subject to several but correlated risks. The results of the simulation analysis suggest that the non-inclusion of the intratemporal correlation between input variables underestimate the risk associated with investment decisions. The methodological contribution of this study illustrates the complexity of the interrelationships between uncertain variables and their impact on the convenience of carrying out this type of business in Chile. The steps for the analysis of economic viability were: First, adjusted probability distributions for stochastic input variables (SIV were simulated and validated. Second, the random values of SIV were used to calculate random values of variables such as production, revenues, costs, depreciation, taxes and net cash flows. Third, the complete stochastic model was simulated with 10,000 iterations using random values for SIV. This result gave information to estimate the probability distributions of the stochastic output variables (SOV such as the net present value, internal rate of return, value at risk, average cost of production, contribution margin and return on capital. Fourth, the complete stochastic model simulation results were used to analyze alternative scenarios and provide the results to decision makers in the form of probabilities, probability distributions, and for the SOV probabilistic forecasts. The main conclusion shown that this project is a profitable alternative investment in fruit trees in

Stochastic simulation of grain growth during continuous casting

International Nuclear Information System (INIS)

Ramirez, A.; Carrillo, F.; Gonzalez, J.L.; Lopez, S.

2006-01-01

The evolution of microstructure is a very important topic in material science engineering because the solidification conditions of steel billets during continuous casting process affect directly the properties of the final products. In this paper a mathematical model is described in order to simulate the dendritic growth using data of real casting operations; here a combination of deterministic and stochastic methods was used as a function of the solidification time of every node in order to create a reconstruction about the morphology of cast structures

Stochastic simulation of grain growth during continuous casting

Energy Technology Data Exchange (ETDEWEB)

Ramirez, A. [Department of Aerounatical Engineering, S.E.P.I., E.S.I.M.E., IPN, Instituto Politecnico Nacional (Unidad Profesional Ticoman), Av. Ticoman 600, Col. Ticoman, C.P.07340 (Mexico)]. E-mail: adalop123@mailbanamex.com; Carrillo, F. [Department of Processing Materials, CICATA-IPN Unidad Altamira Tamps (Mexico); Gonzalez, J.L. [Department of Metallurgy and Materials Engineering, E.S.I.Q.I.E.-IPN (Mexico); Lopez, S. [Department of Molecular Engineering of I.M.P., AP 14-805 (Mexico)

2006-04-15

The evolution of microstructure is a very important topic in material science engineering because the solidification conditions of steel billets during continuous casting process affect directly the properties of the final products. In this paper a mathematical model is described in order to simulate the dendritic growth using data of real casting operations; here a combination of deterministic and stochastic methods was used as a function of the solidification time of every node in order to create a reconstruction about the morphology of cast structures.

A hybrid meta-heuristic algorithm for the vehicle routing problem with stochastic travel times considering the driver's satisfaction

Science.gov (United States)

Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges

2012-05-01

A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.

ENVIRONMENT: a computational platform to stochastically simulate reacting and self-reproducing lipid compartments

Science.gov (United States)

Mavelli, Fabio; Ruiz-Mirazo, Kepa

2010-09-01

'ENVIRONMENT' is a computational platform that has been developed in the last few years with the aim to simulate stochastically the dynamics and stability of chemically reacting protocellular systems. Here we present and describe some of its main features, showing how the stochastic kinetics approach can be applied to study the time evolution of reaction networks in heterogeneous conditions, particularly when supramolecular lipid structures (micelles, vesicles, etc) coexist with aqueous domains. These conditions are of special relevance to understand the origins of cellular, self-reproducing compartments, in the context of prebiotic chemistry and evolution. We contrast our simulation results with real lab experiments, with the aim to bring together theoretical and experimental research on protocell and minimal artificial cell systems.

Study on the mechanism and efficiency of simulated annealing using an LP optimization benchmark problem - 113

International Nuclear Information System (INIS)

Qianqian, Li; Xiaofeng, Jiang; Shaohong, Zhang

2010-01-01

Simulated Annealing Algorithm (SAA) for solving combinatorial optimization problems is a popular method for loading pattern optimization. The main purpose of this paper is to understand the underlying search mechanism of SAA and to study its efficiency. In this study, a general SAA that employs random pair exchange of fuel assemblies to search for the optimum fuel Loading Pattern (LP) is applied to an exhaustively searched LP optimization benchmark problem. All the possible LPs of the benchmark problem have been enumerated and evaluated via the use of the very fast and accurate Hybrid Harmonics and Linear Perturbation (HHLP) method, such that the mechanism of SA for LP optimization can be explicitly analyzed and its search efficiency evaluated. The generic core geometry itself dictates that only a small number LPs can be generated by performing random single pair exchanges and that the LPs are necessarily mostly similar to the initial LP. This phase space effect turns out to be the basic mechanism in SAA that can explain its efficiency and good local search ability. A measure of search efficiency is introduced which shows that the stochastic nature of SAA greatly influences the variability of its search efficiency. It is also found that using fuel assembly k-infinity distribution as a technique to filter the LPs can significantly enhance the SAA search efficiency. (authors)

Stabilizing simulations of complex stochastic representations for quantum dynamical systems

Energy Technology Data Exchange (ETDEWEB)

Perret, C; Petersen, W P, E-mail: wpp@math.ethz.ch [Seminar for Applied Mathematics, ETH, Zurich (Switzerland)

2011-03-04

Path integral representations of quantum dynamics can often be formulated as stochastic differential equations (SDEs). In a series of papers, Corney and Drummond (2004 Phys. Rev. Lett. 93 260401), Deuar and Drummond (2001 Comput. Phys. Commun. 142 442-5), Drummond and Gardnier (1980 J. Phys. A: Math. Gen. 13 2353-68), Gardiner and Zoller (2004 Quantum Noise: A Handbook of Markovian and Non-Markovian Quantum Stochastic Methods with Applications to Quantum Optics (Springer Series in Synergetics) 3rd edn (Berlin: Springer)) and Gilchrist et al (1997 Phys. Rev. A 55 3014-32) and their collaborators have derived SDEs from coherent states representations for density matrices. Computationally, these SDEs are attractive because they seem simple to simulate. They can be quite unstable, however. In this paper, we consider some of the instabilities and propose a few remedies. Particularly, because the variances of the simulated paths typically grow exponentially, the processes become de-localized in relatively short times. Hence, the issues of boundary conditions and stable integration methods become important. We use the Bose-Einstein Hamiltonian as an example. Our results reveal that it is possible to significantly extend integration times and show the periodic structure of certain functionals.

MCdevelop - a universal framework for Stochastic Simulations

Science.gov (United States)

Slawinska, M.; Jadach, S.

2011-03-01

We present MCdevelop, a universal computer framework for developing and exploiting the wide class of Stochastic Simulations (SS) software. This powerful universal SS software development tool has been derived from a series of scientific projects for precision calculations in high energy physics (HEP), which feature a wide range of functionality in the SS software needed for advanced precision Quantum Field Theory calculations for the past LEP experiments and for the ongoing LHC experiments at CERN, Geneva. MCdevelop is a "spin-off" product of HEP to be exploited in other areas, while it will still serve to develop new SS software for HEP experiments. Typically SS involve independent generation of large sets of random "events", often requiring considerable CPU power. Since SS jobs usually do not share memory it makes them easy to parallelize. The efficient development, testing and running in parallel SS software requires a convenient framework to develop software source code, deploy and monitor batch jobs, merge and analyse results from multiple parallel jobs, even before the production runs are terminated. Throughout the years of development of stochastic simulations for HEP, a sophisticated framework featuring all the above mentioned functionality has been implemented. MCdevelop represents its latest version, written mostly in C++ (GNU compiler gcc). It uses Autotools to build binaries (optionally managed within the KDevelop 3.5.3 Integrated Development Environment (IDE)). It uses the open-source ROOT package for histogramming, graphics and the mechanism of persistency for the C++ objects. MCdevelop helps to run multiple parallel jobs on any computer cluster with NQS-type batch system. Program summaryProgram title:MCdevelop Catalogue identifier: AEHW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http

Combined Simulated Annealing Algorithm for the Discrete Facility Location Problem

Directory of Open Access Journals (Sweden)

Jin Qin

2012-01-01

Full Text Available The combined simulated annealing (CSA algorithm was developed for the discrete facility location problem (DFLP in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.

Quantum simulation of a quantum stochastic walk

Science.gov (United States)

Govia, Luke C. G.; Taketani, Bruno G.; Schuhmacher, Peter K.; Wilhelm, Frank K.

2017-03-01

The study of quantum walks has been shown to have a wide range of applications in areas such as artificial intelligence, the study of biological processes, and quantum transport. The quantum stochastic walk (QSW), which allows for incoherent movement of the walker, and therefore, directionality, is a generalization on the fully coherent quantum walk. While a QSW can always be described in Lindblad formalism, this does not mean that it can be microscopically derived in the standard weak-coupling limit under the Born-Markov approximation. This restricts the class of QSWs that can be experimentally realized in a simple manner. To circumvent this restriction, we introduce a technique to simulate open system evolution on a fully coherent quantum computer, using a quantum trajectories style approach. We apply this technique to a broad class of QSWs, and show that they can be simulated with minimal experimental resources. Our work opens the path towards the experimental realization of QSWs on large graphs with existing quantum technologies.

Conditional Stochastic Models in Reduced Space: Towards Efficient Simulation of Tropical Cyclone Precipitation Patterns

Science.gov (United States)

Dodov, B.

2017-12-01

Stochastic simulation of realistic and statistically robust patterns of Tropical Cyclone (TC) induced precipitation is a challenging task. It is even more challenging in a catastrophe modeling context, where tens of thousands of typhoon seasons need to be simulated in order to provide a complete view of flood risk. Ultimately, one could run a coupled global climate model and regional Numerical Weather Prediction (NWP) model, but this approach is not feasible in the catastrophe modeling context and, most importantly, may not provide TC track patterns consistent with observations. Rather, we propose to leverage NWP output for the observed TC precipitation patterns (in terms of downscaled reanalysis 1979-2015) collected on a Lagrangian frame along the historical TC tracks and reduced to the leading spatial principal components of the data. The reduced data from all TCs is then grouped according to timing, storm evolution stage (developing, mature, dissipating, ETC transitioning) and central pressure and used to build a dictionary of stationary (within a group) and non-stationary (for transitions between groups) covariance models. Provided that the stochastic storm tracks with all the parameters describing the TC evolution are already simulated, a sequence of conditional samples from the covariance models chosen according to the TC characteristics at a given moment in time are concatenated, producing a continuous non-stationary precipitation pattern in a Lagrangian framework. The simulated precipitation for each event is finally distributed along the stochastic TC track and blended with a non-TC background precipitation using a data assimilation technique. The proposed framework provides means of efficient simulation (10000 seasons simulated in a couple of days) and robust typhoon precipitation patterns consistent with observed regional climate and visually undistinguishable from high resolution NWP output. The framework is used to simulate a catalog of 10000 typhoon

Simulated annealing of displacement cascades in FCC metals. 1. Beeler cascades

International Nuclear Information System (INIS)

Doran, D.G.; Burnett, R.A.

1974-09-01

An important source of damage to structural materials in fast reactors is the displacement of atoms from normal lattice sites. A high energy neutron may impart sufficient energy to an atom to initiate a displacement cascade consisting of a localized high density of hundreds of interstitials and vacancies. These defects subsequently interact to form clusters and to reduce their density by mutual annihilation. This short term annealing of an isolated cascade has been simulated at high and low temperatures using a correlated random walk model. The cascade representations used were developed by Beeler and the point defect properties were based on the model of γ-iron by Johnson. Low temperature anneals, characterized by no vacancy migration and a 104 site annihilation region (AR), resulted in 49 defect pairs at 20 keV and 11 pairs at 5 keV. High temperature anneals, characterized by both interstitial and vacancy migration and a 32 site AR, resulted in 68 pairs at 20 keV and 18 pairs at 5 keV when no cluster dissociation was permitted; most of the vacancies were in immobile clusters. These high temperature values dropped to 40 and 14 upon dissolution of the vacancy clusters. Parameter studies showed that, at a given temperature, the large AR resulted in about one-half as many defects as the small AR. Cluster size distributions and examples of spatial configurations are included. (U.S.)

Modeling Group Perceptions Using Stochastic Simulation: Scaling Issues in the Multiplicative AHP

DEFF Research Database (Denmark)

Barfod, Michael Bruhn; van den Honert, Robin; Salling, Kim Bang

2016-01-01

This paper proposes a new decision support approach for applying stochastic simulation to the multiplicative analytic hierarchy process (AHP) in order to deal with issues concerning the scale parameter. The paper suggests a new approach that captures the influence from the scale parameter by maki...

A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

DEFF Research Database (Denmark)

Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

2011-01-01

The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology of the...

Stochastic Processes and Queueing Theory used in Cloud Computer Performance Simulations

Directory of Open Access Journals (Sweden)

Florin-Catalin ENACHE

2015-10-01

Full Text Available The growing character of the cloud business has manifested exponentially in the last 5 years. The capacity managers need to concentrate on a practical way to simulate the random demands a cloud infrastructure could face, even if there are not too many mathematical tools to simulate such demands.This paper presents an introduction into the most important stochastic processes and queueing theory concepts used for modeling computer performance. Moreover, it shows the cases where such concepts are applicable and when not, using clear programming examples on how to simulate a queue, and how to use and validate a simulation, when there are no mathematical concepts to back it up.

Natural tracer test simulation by stochastic particle tracking method

International Nuclear Information System (INIS)

Ackerer, P.; Mose, R.; Semra, K.

1990-01-01

Stochastic particle tracking methods are well adapted to 3D transport simulations where discretization requirements of other methods usually cannot be satisfied. They do need a very accurate approximation of the velocity field. The described code is based on the mixed hybrid finite element method (MHFEM) to calculated the piezometric and velocity field. The random-walk method is used to simulate mass transport. The main advantages of the MHFEM over FD or FE are the simultaneous calculation of pressure and velocity, which are considered as unknowns; the possibility of interpolating velocities everywhere; and the continuity of the normal component of the velocity vector from one element to another. For these reasons, the MHFEM is well adapted for particle tracking methods. After a general description of the numerical methods, the model is used to simulate the observations made during the Twin Lake Tracer Test in 1983. A good match is found between observed and simulated heads and concentrations. (Author) (12 refs., 4 figs.)

Simulation of Higher-Order Electrical Circuits with Stochastic Parameters via SDEs

Directory of Open Access Journals (Sweden)

BRANCIK, L.

2013-02-01

Full Text Available The paper deals with a technique for the simulation of higher-order electrical circuits with parameters varying randomly. The principle consists in the utilization of the theory of stochastic differential equations (SDE, namely the vector form of the ordinary SDEs. Random changes of both excitation voltage and some parameters of passive circuit elements are considered, and circuit responses are analyzed. The voltage and/or current responses are computed and represented in the form of the sample means accompanied by their confidence intervals to provide reliable estimates. The method is applied to analyze responses of the circuit models of optional orders, specially those consisting of a cascade connection of the RLGC networks. To develop the model equations the state-variable method is used, afterwards a corresponding vector SDE is formulated and a stochastic Euler numerical method applied. To verify the results the deterministic responses are also computed by the help of the PSpice simulator or the numerical inverse Laplace transforms (NILT procedure in MATLAB, while removing random terms from the circuit model.

Temporary Workforce Planning with Firm Contracts: A Model and a Simulated Annealing Heuristic

Directory of Open Access Journals (Sweden)

Muhammad Al-Salamah

2011-01-01

Full Text Available The aim of this paper is to introduce a model for temporary staffing when temporary employment is managed by firm contracts and to propose a simulated annealing-based method to solve the model. Temporary employment is a policy frequently used to adjust the working hour capacity to fluctuating demand. Temporary workforce planning models have been unnecessarily simplified to account for only periodic hiring and laying off; a company can review its workforce requirement every period and make hire-fire decisions accordingly, usually with a layoff cost. We present a more realistic temporary workforce planning model that assumes a firm contract between the worker and the company, which can extend to several periods. The model assumes the traditional constraints, such as inventory balance constraints, worker availability, and labor hour mix. The costs are the inventory holding cost, training cost of the temporary workers, and the backorder cost. The mixed integer model developed for this case has been found to be difficult to solve even for small problem sizes; therefore, a simulated annealing algorithm is proposed to solve the mixed integer model. The performance of the SA algorithm is compared with the CPLEX solution.

Some simulation aspects, from molecular systems to stochastic geometries of pebble bed reactors

International Nuclear Information System (INIS)

Mazzolo, A.

2009-06-01

After a brief presentation of his teaching and supervising activities, the author gives an overview of his research activities: investigation of atoms under high intensity magnetic field (investigation of the electronic structure under these fields), studies of theoretical and numerical electrochemistry (simulation coupling molecular dynamics and quantum calculations, comprehensive simulations of molecular dynamics), and studies relating stochastic geometry and neutron science

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

Science.gov (United States)

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at

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.

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.

PENJADWALAN JOB SHOP STATIK DENGAN METODE SIMULATED ANNEALING UNTUK MEMINIMASI WAKTU MAKESPAN

Directory of Open Access Journals (Sweden)

Moh.Husen

2015-10-01

Full Text Available Penjadwalan bagi perusahaan adalah aspek yang sangat penting, karena penjadwalan merupakan salah satu elemen perencanaan dan pengendalian produksi, sehingga perusahaan dapat mengirim barang sesuai dengan waktu yang telah ditentukan, agar diperoleh waktu total penyelesaian yang minimum. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA dengan bantuan Matlab diharapkan dapat menghasilkan waktu total penyelesaian (makespan lebih cepat dari penjadwalan yang ada pada perusahaan. Metode SA mensimulasikan proses annealing pada pembuatan materi yang terdiri dari butiran Kristal atau logam. Tujuan dari proses ini adalah menghasilkan struktur kristal yang baik dengan menggunakan energi seminimal mungkin. Permasalahan yang dihadapi oleh perusahaan adalah perusahaan belum mempertimbangkan makespan dalam penyelesaian produk dan penjadwalan produksi untuk produk paket satu rumah kos-kosan. Hal ini berdasarkan data produksi yang terjadi keterlambatan dilihat dari waktu penyelesaian (makespan produksi, sehingga perusahaan harus menambah 2-5 hari lagi untuk bisa menyelesaikan keseluruhan produk. Dengan menggunakan metode SA menghasilkan makespan 23 jam, lebih cepat 2 jam dari pada penjadwalan awal.

Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations

Energy Technology Data Exchange (ETDEWEB)

Hepburn, I.; De Schutter, E., E-mail: erik@oist.jp [Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904 0495 (Japan); Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610 (Belgium); Chen, W. [Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904 0495 (Japan)

2016-08-07

Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realistic biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.

Stochastic stresses in granular matter simulated by dripping identical ellipses into plane silo

DEFF Research Database (Denmark)

Berntsen, Kasper Nikolaj; Ditlevsen, Ove Dalager

2000-01-01

A two-dimensional silo pressure model-problem is investigated by molecular dynamics simulations. A plane silo container is filled by a granular matter consisting of congruent elliptic particles dropped one by one into the silo. A suitable energy absorbing contact force mechanism is activatedduring...... the granular matter in the silo are compared to thesolution of a stochastic equilibrium differential equation. In this equation the stochasticity source is a homogeneouswhite noise gamma-distributed side pressure factor field along the walls. This is a generalization of the deterministic side pressure factor...... proposed by Janssen in 1895. The stochastic Janssen factor model is shown to be fairly consistentwith the observations from which the mean and the intensity of the white noise is estimated by the method of maximumlikelihood using the properties of the gamma-distribution. Two wall friction coefficients...

Stochastic Simulation of Cardiac Ventricular Myocyte Calcium Dynamics and Waves

OpenAIRE

Tuan, Hoang-Trong Minh; Williams, George S. B.; Chikando, Aristide C.; Sobie, Eric A.; Lederer, W. Jonathan; Jafri, M. Saleet

2011-01-01

A three dimensional model of calcium dynamics in the rat ventricular myocyte was developed to study the mechanism of calcium homeostasis and pathological calcium dynamics during calcium overload. The model contains 20,000 calcium release units (CRUs) each containing 49 ryanodine receptors. The model simulates calcium sparks with a realistic spontaneous calcium spark rate. It suggests that in addition to the calcium spark-based leak, there is an invisible calcium leak caused by the stochastic ...

Development of Fast-Time Stochastic Airport Ground and Runway Simulation Model and Its Traffic Analysis

Directory of Open Access Journals (Sweden)

Ryota Mori

2015-01-01

Full Text Available Airport congestion, in particular congestion of departure aircraft, has already been discussed by other researches. Most solutions, though, fail to account for uncertainties. Since it is difficult to remove uncertainties of the operations in the real world, a strategy should be developed assuming such uncertainties exist. Therefore, this research develops a fast-time stochastic simulation model used to validate various methods in order to decrease airport congestion level under existing uncertainties. The surface movement data is analyzed first, and the uncertainty level is obtained. Next, based on the result of data analysis, the stochastic simulation model is developed. The model is validated statistically and the characteristics of airport operation under existing uncertainties are investigated.

A study on three dimensional layout design by the simulated annealing method

International Nuclear Information System (INIS)

Jang, Seung Ho

2008-01-01

Modern engineered products are becoming increasingly complicated and most consumers prefer compact designs. Layout design plays an important role in many engineered products. The objective of this study is to suggest a method to apply the simulated annealing method to the arbitrarily shaped three-dimensional component layout design problem. The suggested method not only optimizes the packing density but also satisfies constraint conditions among the components. The algorithm and its implementation as suggested in this paper are extendable to other research objectives

Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm

Science.gov (United States)

Lee, C.

2013-12-01

The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.

A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuations

International Nuclear Information System (INIS)

Kaplani, E.; Kaplanis, S.

2012-01-01

Highlights: ► Solar radiation data for European cities follow the Extreme Value or Weibull distribution. ► Simulation model for the sizing of SAPV systems based on energy balance and stochastic analysis. ► Simulation of PV Generator-Loads-Battery Storage System performance for all months. ► Minimum peak power and battery capacity required for reliable SAPV sizing for various European cities. ► Peak power and battery capacity reduced by more than 30% for operation 95% success rate. -- Abstract: The large fluctuations observed in the daily solar radiation profiles affect highly the reliability of the PV system sizing. Increasing the reliability of the PV system requires higher installed peak power (P m ) and larger battery storage capacity (C L ). This leads to increased costs, and makes PV technology less competitive. This research paper presents a new stochastic simulation model for stand-alone PV systems, developed to determine the minimum installed P m and C L for the PV system to be energy independent. The stochastic simulation model developed, makes use of knowledge acquired from an in-depth statistical analysis of the solar radiation data for the site, and simulates the energy delivered, the excess energy burnt, the load profiles and the state of charge of the battery system for the month the sizing is applied, and the PV system performance for the entire year. The simulation model provides the user with values for the autonomy factor d, simulating PV performance in order to determine the minimum P m and C L depending on the requirements of the application, i.e. operation with critical or non-critical loads. The model makes use of NASA’s Surface meteorology and Solar Energy database for the years 1990–2004 for various cities in Europe with a different climate. The results obtained with this new methodology indicate a substantial reduction in installed peak power and battery capacity, both for critical and non-critical operation, when compared to

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.

Coarse-grained stochastic processes and kinetic Monte Carlo simulators for the diffusion of interacting particles

Science.gov (United States)

Katsoulakis, Markos A.; Vlachos, Dionisios G.

2003-11-01

We derive a hierarchy of successively coarse-grained stochastic processes and associated coarse-grained Monte Carlo (CGMC) algorithms directly from the microscopic processes as approximations in larger length scales for the case of diffusion of interacting particles on a lattice. This hierarchy of models spans length scales between microscopic and mesoscopic, satisfies a detailed balance, and gives self-consistent fluctuation mechanisms whose noise is asymptotically identical to the microscopic MC. Rigorous, detailed asymptotics justify and clarify these connections. Gradient continuous time microscopic MC and CGMC simulations are compared under far from equilibrium conditions to illustrate the validity of our theory and delineate the errors obtained by rigorous asymptotics. Information theory estimates are employed for the first time to provide rigorous error estimates between the solutions of microscopic MC and CGMC, describing the loss of information during the coarse-graining process. Simulations under periodic boundary conditions are used to verify the information theory error estimates. It is shown that coarse-graining in space leads also to coarse-graining in time by q2, where q is the level of coarse-graining, and overcomes in part the hydrodynamic slowdown. Operation counting and CGMC simulations demonstrate significant CPU savings in continuous time MC simulations that vary from q3 for short potentials to q4 for long potentials. Finally, connections of the new coarse-grained stochastic processes to stochastic mesoscopic and Cahn-Hilliard-Cook models are made.

First-order design of geodetic networks using the simulated annealing method

Science.gov (United States)

Berné, J. L.; Baselga, S.

2004-09-01

The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.

Compatible topologies and parameters for NMR structure determination of carbohydrates by simulated annealing

OpenAIRE

Feng, Yingang

2017-01-01

The use of NMR methods to determine the three-dimensional structures of carbohydrates and glycoproteins is still challenging, in part because of the lack of standard protocols. In order to increase the convenience of structure determination, the topology and parameter files for carbohydrates in the program Crystallography & NMR System (CNS) were investigated and new files were developed to be compatible with the standard simulated annealing protocols for proteins and nucleic acids. Recalculat...

Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

KAUST Repository

Moraes, Alvaro

2015-01-01

Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale. During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number. Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others. 4 This PhD thesis is focused on novel fast simulation algorithms and statistical inference methods for SRNs. Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology. Regarding the statistical inference

Comparison of Lasserre's Measure-based Bounds for Polynomial Optimization to Bounds Obtained by Simulated Annealing

NARCIS (Netherlands)

de Klerk, Etienne; Laurent, Monique

We consider the problem of minimizing a continuous function f over a compact set K. We compare the hierarchy of upper bounds proposed by Lasserre in [SIAM J. Optim. 21(3) (2011), pp. 864-885] to bounds that may be obtained from simulated annealing. We show that, when f is a polynomial and K a convex

Time-ordered product expansions for computational stochastic system biology

International Nuclear Information System (INIS)

Mjolsness, Eric

2013-01-01

The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie’s stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems. (paper)

Temperature Scaling Law for Quantum Annealing Optimizers.

Science.gov (United States)

Albash, Tameem; Martin-Mayor, Victor; Hen, Itay

2017-09-15

Physical implementations of quantum annealing unavoidably operate at finite temperatures. We point to a fundamental limitation of fixed finite temperature quantum annealers that prevents them from functioning as competitive scalable optimizers and show that to serve as optimizers annealer temperatures must be appropriately scaled down with problem size. We derive a temperature scaling law dictating that temperature must drop at the very least in a logarithmic manner but also possibly as a power law with problem size. We corroborate our results by experiment and simulations and discuss the implications of these to practical annealers.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis.

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-21

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Neighbourhood generation mechanism applied in simulated annealing to job shop scheduling problems

Science.gov (United States)

Cruz-Chávez, Marco Antonio

2015-11-01

This paper presents a neighbourhood generation mechanism for the job shop scheduling problems (JSSPs). In order to obtain a feasible neighbour with the generation mechanism, it is only necessary to generate a permutation of an adjacent pair of operations in a scheduling of the JSSP. If there is no slack time between the adjacent pair of operations that is permuted, then it is proven, through theory and experimentation, that the new neighbour (schedule) generated is feasible. It is demonstrated that the neighbourhood generation mechanism is very efficient and effective in a simulated annealing.

Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2015-01-07

Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

Science.gov (United States)

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Adaptive MANET Multipath Routing Algorithm Based on the Simulated Annealing Approach

Directory of Open Access Journals (Sweden)

Sungwook Kim

2014-01-01

Full Text Available Mobile ad hoc network represents a system of wireless mobile nodes that can freely and dynamically self-organize network topologies without any preexisting communication infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. In this paper, a new multipath routing scheme is proposed by employing simulated annealing approach. The proposed metaheuristic approach can achieve greater and reciprocal advantages in a hostile dynamic real world network situation. Therefore, the proposed routing scheme is a powerful method for finding an effective solution into the conflict mobile ad hoc network routing problem. Simulation results indicate that the proposed paradigm adapts best to the variation of dynamic network situations. The average remaining energy, network throughput, packet loss probability, and traffic load distribution are improved by about 10%, 10%, 5%, and 10%, respectively, more than the existing schemes.

Calibration of semi-stochastic procedure for simulating high-frequency ground motions

Science.gov (United States)

Seyhan, Emel; Stewart, Jonathan P.; Graves, Robert

2013-01-01

Broadband ground motion simulation procedures typically utilize physics-based modeling at low frequencies, coupled with semi-stochastic procedures at high frequencies. The high-frequency procedure considered here combines deterministic Fourier amplitude spectra (dependent on source, path, and site models) with random phase. Previous work showed that high-frequency intensity measures from this simulation methodology attenuate faster with distance and have lower intra-event dispersion than in empirical equations. We address these issues by increasing crustal damping (Q) to reduce distance attenuation bias and by introducing random site-to-site variations to Fourier amplitudes using a lognormal standard deviation ranging from 0.45 for Mw  100 km).

Simulated annealing with restart strategy for the blood pickup routing problem

Science.gov (United States)

Yu, V. F.; Iswari, T.; Normasari, N. M. E.; Asih, A. M. S.; Ting, H.

2018-04-01

This study develops a simulated annealing heuristic with restart strategy (SA_RS) for solving the blood pickup routing problem (BPRP). BPRP minimizes the total length of the routes for blood bag collection between a blood bank and a set of donation sites, each associated with a time window constraint that must be observed. The proposed SA_RS is implemented in C++ and tested on benchmark instances of the vehicle routing problem with time windows to verify its performance. The algorithm is then tested on some newly generated BPRP instances and the results are compared with those obtained by CPLEX. Experimental results show that the proposed SA_RS heuristic effectively solves BPRP.

A micro-macro acceleration method for the Monte Carlo simulation of stochastic differential equations

DEFF Research Database (Denmark)

Debrabant, Kristian; Samaey, Giovanni; Zieliński, Przemysław

2017-01-01

We present and analyse a micro-macro acceleration method for the Monte Carlo simulation of stochastic differential equations with separation between the (fast) time-scale of individual trajectories and the (slow) time-scale of the macroscopic function of interest. The algorithm combines short...

STOCHASTIC SIMULATION FOR BUFFELGRASS (Cenchrus ciliaris L.) PASTURES IN MARIN, N. L., MEXICO

OpenAIRE

José Romualdo Martínez-López; Erasmo Gutierrez-Ornelas; Miguel Angel Barrera-Silva; Rafael Retes-López

2014-01-01

A stochastic simulation model was constructed to determine the response of net primary production of buffelgrass (Cenchrus ciliaris L.) and its dry matter intake by cattle, in Marín, NL, México. Buffelgrass is very important for extensive livestock industry in arid and semiarid areas of northeastern Mexico. To evaluate the behavior of the model by comparing the model results with those reported in the literature was the objective in this experiment. Model simulates the monthly production of...

Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Directory of Open Access Journals (Sweden)

Adrián A. Toncovich

2019-01-01

Full Text Available The competition manufacturing companies face has driven the development of novel and efficient methods that enhance the decision making process. In this work, a specific flow shop scheduling problem of practical interest in the industry is presented and formalized using a mathematical programming model. The problem considers a manufacturing system arranged as a work cell that takes into account the transport operations of raw material and final products between the manufacturing cell and warehouses. For solving this problem, we present a multiobjective metaheuristic strategy based on simulated annealing, the Pareto Archived Simulated Annealing (PASA. We tested this strategy on two kinds of benchmark problem sets proposed by the authors. The first group is composed by small-sized problems. On these tests, PASA was able to obtain optimal or near-optimal solutions in significantly short computing times. In order to complete the analysis, we compared these results to the exact Pareto front of the instances obtained with augmented ε-constraint method. Then, we also tested the algorithm in a set of larger problems to evaluate its performance in more extensive search spaces. We performed this assessment through an analysis of the hypervolume metric. Both sets of tests showed the competitiveness of the Pareto Archived Simulated Annealing to efficiently solve this problem and obtain good quality solutions while using reasonable computational resources.

MarkoLAB: A simulator to study ionic channel's stochastic behavior.

Science.gov (United States)

da Silva, Robson Rodrigues; Goroso, Daniel Gustavo; Bers, Donald M; Puglisi, José Luis

2017-08-01

Mathematical models of the cardiac cell have started to include markovian representations of the ionic channels instead of the traditional Hodgkin & Huxley formulations. There are many reasons for this: Markov models are not restricted to the idea of independent gates defining the channel, they allow more complex description with specific transitions between open, closed or inactivated states, and more importantly those states can be closely related to the underlying channel structure and conformational changes. We used the LabVIEW ® and MATLAB ® programs to implement the simulator MarkoLAB that allow a dynamical 3D representation of the markovian model of the channel. The Monte Carlo simulation was used to implement the stochastic transitions among states. The user can specify the voltage protocol by setting the holding potential, the step-to voltage and the duration of the stimuli. The most studied feature of a channel is the current flowing through it. This happens when the channel stays in the open state, but most of the time, as revealed by the low open probability values, the channel remains on the inactive or closed states. By focusing only when the channel enters or leaves the open state we are missing most of its activity. MarkoLAB proved to be quite useful to visualize the whole behavior of the channel and not only when the channel produces a current. Such dynamic representation provides more complete information about channel kinetics and will be a powerful tool to demonstrate the effect of gene mutations or drugs on the channel function. MarkoLAB provides an original way of visualizing the stochastic behavior of a channel. It clarifies concepts, such as recovery from inactivation, calcium- versus voltage-dependent inactivation, and tail currents. It is not restricted to ionic channels only but it can be extended to other transporters, such as exchangers and pumps. This program is intended as a didactical tool to illustrate the dynamical behavior of a

Monte Carlo simulation of induction time and metastable zone width; stochastic or deterministic?

Science.gov (United States)

Kubota, Noriaki

2018-03-01

The induction time and metastable zone width (MSZW) measured for small samples (say 1 mL or less) both scatter widely. Thus, these two are observed as stochastic quantities. Whereas, for large samples (say 1000 mL or more), the induction time and MSZW are observed as deterministic quantities. The reason for such experimental differences is investigated with Monte Carlo simulation. In the simulation, the time (under isothermal condition) and supercooling (under polythermal condition) at which a first single crystal is detected are defined as the induction time t and the MSZW ΔT for small samples, respectively. The number of crystals just at the moment of t and ΔT is unity. A first crystal emerges at random due to the intrinsic nature of nucleation, accordingly t and ΔT become stochastic. For large samples, the time and supercooling at which the number density of crystals N/V reaches a detector sensitivity (N/V)det are defined as t and ΔT for isothermal and polythermal conditions, respectively. The points of t and ΔT are those of which a large number of crystals have accumulated. Consequently, t and ΔT become deterministic according to the law of large numbers. Whether t and ΔT may stochastic or deterministic in actual experiments should not be attributed to change in nucleation mechanisms in molecular level. It could be just a problem caused by differences in the experimental definition of t and ΔT.

Optimization of cladding parameters for resisting corrosion on low carbon steels using simulated annealing algorithm

Science.gov (United States)

Balan, A. V.; Shivasankaran, N.; Magibalan, S.

2018-04-01

Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.

Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing

International Nuclear Information System (INIS)

Zhang Pengpeng; Dean, David; Metzger, Andrew; Sibata, Claudio

2001-01-01

We present a method for generating optimized Gamma Knife trade mark sign (Elekta, Stockholm, Sweden) radiosurgery treatment plans. This semiautomatic method produces a highly conformal shot packing plan for the irradiation of an intracranial tumor. We simulate optimal treatment planning criteria with a probability function that is linked to every voxel in a volumetric (MR or CT) region of interest. This sigmoidal P + parameter models the requirement of conformality (i.e., tumor ablation and normal tissue sparing). After determination of initial radiosurgery treatment parameters, a guided evolutionary simulated annealing (GESA) algorithm is used to find the optimal size, position, and weight for each shot. The three-dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual shot packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a more conformal plan, which also reduces redundancy, and saves treatment administration time

Discrete-State Simulated Annealing For Traveling-Wave Tube Slow-Wave Circuit Optimization

Science.gov (United States)

Wilson, Jeffrey D.; Bulson, Brian A.; Kory, Carol L.; Williams, W. Dan (Technical Monitor)

2001-01-01

Algorithms based on the global optimization technique of simulated annealing (SA) have proven useful in designing traveling-wave tube (TWT) slow-wave circuits for high RF power efficiency. The characteristic of SA that enables it to determine a globally optimized solution is its ability to accept non-improving moves in a controlled manner. In the initial stages of the optimization, the algorithm moves freely through configuration space, accepting most of the proposed designs. This freedom of movement allows non-intuitive designs to be explored rather than restricting the optimization to local improvement upon the initial configuration. As the optimization proceeds, the rate of acceptance of non-improving moves is gradually reduced until the algorithm converges to the optimized solution. The rate at which the freedom of movement is decreased is known as the annealing or cooling schedule of the SA algorithm. The main disadvantage of SA is that there is not a rigorous theoretical foundation for determining the parameters of the cooling schedule. The choice of these parameters is highly problem dependent and the designer needs to experiment in order to determine values that will provide a good optimization in a reasonable amount of computational time. This experimentation can absorb a large amount of time especially when the algorithm is being applied to a new type of design. In order to eliminate this disadvantage, a variation of SA known as discrete-state simulated annealing (DSSA), was recently developed. DSSA provides the theoretical foundation for a generic cooling schedule which is problem independent, Results of similar quality to SA can be obtained, but without the extra computational time required to tune the cooling parameters. Two algorithm variations based on DSSA were developed and programmed into a Microsoft Excel spreadsheet graphical user interface (GUI) to the two-dimensional nonlinear multisignal helix traveling-wave amplifier analysis program TWA3

A simulated annealing approach for redesigning a warehouse network problem

Science.gov (United States)

Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia

2017-09-01

Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.

Population annealing: Theory and application in spin glasses

OpenAIRE

Wang, Wenlong; Machta, Jonathan; Katzgraber, Helmut G.

2015-01-01

Population annealing is an efficient sequential Monte Carlo algorithm for simulating equilibrium states of systems with rough free energy landscapes. The theory of population annealing is presented, and systematic and statistical errors are discussed. The behavior of the algorithm is studied in the context of large-scale simulations of the three-dimensional Ising spin glass and the performance of the algorithm is compared to parallel tempering. It is found that the two algorithms are similar ...

Enhanced Simulated Annealing for Solving Aggregate Production Planning

Directory of Open Access Journals (Sweden)

Mohd Rizam Abu Bakar

2016-01-01

Full Text Available Simulated annealing (SA has been an effective means that can address difficulties related to optimisation problems. SA is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised by SA. During the course of optimising for the APP problem, it uncovered that the capability of SA was inadequate and its performance was substandard, particularly for a sizable controlled APP problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modified SA (MSA is proposed. We attempt to augment the search space by starting with N+1 solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard SA and harmony search (HS, the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to SA and HS, MSA offers better quality solutions with regard to convergence and accuracy.

Modernizing quantum annealing using local searches

International Nuclear Information System (INIS)

Chancellor, Nicholas

2017-01-01

I describe how real quantum annealers may be used to perform local (in state space) searches around specified states, rather than the global searches traditionally implemented in the quantum annealing algorithm (QAA). Such protocols will have numerous advantages over simple quantum annealing. By using such searches the effect of problem mis-specification can be reduced, as only energy differences between the searched states will be relevant. The QAA is an analogue of simulated annealing, a classical numerical technique which has now been superseded. Hence, I explore two strategies to use an annealer in a way which takes advantage of modern classical optimization algorithms. Specifically, I show how sequential calls to quantum annealers can be used to construct analogues of population annealing and parallel tempering which use quantum searches as subroutines. The techniques given here can be applied not only to optimization, but also to sampling. I examine the feasibility of these protocols on real devices and note that implementing such protocols should require minimal if any change to the current design of the flux qubit-based annealers by D-Wave Systems Inc. I further provide proof-of-principle numerical experiments based on quantum Monte Carlo that demonstrate simple examples of the discussed techniques. (paper)

STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies

Directory of Open Access Journals (Sweden)

Hepburn Iain

2012-05-01

Full Text Available Abstract Background Models of cellular molecular systems are built from components such as biochemical reactions (including interactions between ligands and membrane-bound proteins, conformational changes and active and passive transport. A discrete, stochastic description of the kinetics is often essential to capture the behavior of the system accurately. Where spatial effects play a prominent role the complex morphology of cells may have to be represented, along with aspects such as chemical localization and diffusion. This high level of detail makes efficiency a particularly important consideration for software that is designed to simulate such systems. Results We describe STEPS, a stochastic reaction–diffusion simulator developed with an emphasis on simulating biochemical signaling pathways accurately and efficiently. STEPS supports all the above-mentioned features, and well-validated support for SBML allows many existing biochemical models to be imported reliably. Complex boundaries can be represented accurately in externally generated 3D tetrahedral meshes imported by STEPS. The powerful Python interface facilitates model construction and simulation control. STEPS implements the composition and rejection method, a variation of the Gillespie SSA, supporting diffusion between tetrahedral elements within an efficient search and update engine. Additional support for well-mixed conditions and for deterministic model solution is implemented. Solver accuracy is confirmed with an original and extensive validation set consisting of isolated reaction, diffusion and reaction–diffusion systems. Accuracy imposes upper and lower limits on tetrahedron sizes, which are described in detail. By comparing to Smoldyn, we show how the voxel-based approach in STEPS is often faster than particle-based methods, with increasing advantage in larger systems, and by comparing to MesoRD we show the efficiency of the STEPS implementation. Conclusion STEPS simulates

An intelligent stochastic optimization routine for nuclear fuel cycle design

International Nuclear Information System (INIS)

Parks, G.T.

1990-01-01

A simulated annealing (Metropolis algorithm) optimization routine named AMETROP, which has been developed for use on realistic nuclear fuel cycle problems, is introduced. Each stage of the algorithm is described and the means by which it overcomes or avoids the difficulties posed to conventional optimization routines by such problems are explained. Special attention is given to innovations that enhance AMETROP's performance both through artificial intelligence features, in which the routine uses the accumulation of data to influence its future actions, and through a family of simple performance aids, which allow the designer to use his heuristic knowledge to guide the routine's essentially random search. Using examples from a typical fuel cycle optimization problem, the performance of the stochastic Metropolis algorithm is compared to that of the only suitable deterministic routine in a standard software library, showing AMETROP to have many advantages

Electrode Materials, Thermal Annealing Sequences, and Lateral/Vertical Phase Separation of Polymer Solar Cells from Multiscale Molecular Simulations

KAUST Repository

Lee, Cheng-Kuang

2014-12-10

© 2014 American Chemical Society. The nanomorphologies of the bulk heterojunction (BHJ) layer of polymer solar cells are extremely sensitive to the electrode materials and thermal annealing conditions. In this work, the correlations of electrode materials, thermal annealing sequences, and resultant BHJ nanomorphological details of P3HT:PCBM BHJ polymer solar cell are studied by a series of large-scale, coarse-grained (CG) molecular simulations of system comprised of PEDOT:PSS/P3HT:PCBM/Al layers. Simulations are performed for various configurations of electrode materials as well as processing temperature. The complex CG molecular data are characterized using a novel extension of our graph-based framework to quantify morphology and establish a link between morphology and processing conditions. Our analysis indicates that vertical phase segregation of P3HT:PCBM blend strongly depends on the electrode material and thermal annealing schedule. A thin P3HT-rich film is formed on the top, regardless of bottom electrode material, when the BHJ layer is exposed to the free surface during thermal annealing. In addition, preferential segregation of P3HT chains and PCBM molecules toward PEDOT:PSS and Al electrodes, respectively, is observed. Detailed morphology analysis indicated that, surprisingly, vertical phase segregation does not affect the connectivity of donor/acceptor domains with respective electrodes. However, the formation of P3HT/PCBM depletion zones next to the P3HT/PCBM-rich zones can be a potential bottleneck for electron/hole transport due to increase in transport pathway length. Analysis in terms of fraction of intra- and interchain charge transports revealed that processing schedule affects the average vertical orientation of polymer chains, which may be crucial for enhanced charge transport, nongeminate recombination, and charge collection. The present study establishes a more detailed link between processing and morphology by combining multiscale molecular

A stochastic model for the simulation of wind turbine blades in static stall

DEFF Research Database (Denmark)

Bertagnolio, Franck; Rasmussen, Flemming; Sørensen, Niels N.

2010-01-01

The aim of this work is to improve aeroelastic simulation codes by accounting for the unsteady aerodynamic forces that a blade experiences in static stall. A model based on a spectral representation of the aerodynamic lift force is defined. The drag and pitching moment are derived using...... a conditional simulation technique for stochastic processes. The input data for the model can be collected either from measurements or from numerical results from a Computational Fluid Dynamics code for airfoil sections at constant angles of attack. An analysis of such data is provided, which helps to determine...

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

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.

Simulated annealing CFAR threshold selection for South African ship detection in ASAR imagery

CSIR Research Space (South Africa)

Schwegmann, CP

2014-07-01

Full Text Available ALTER CURRENT THRESHOLD PLANE IF CANDIDATE IS BETTER IF CANDIDATE IS WORSE IF (RANDOM NUMBER < BOLTZMANN PROBABILITY) Fig. 3. The iterative procedure of Simulated Annealing. Starting at some initial threshold plane Ti (x, y) each iteration tests... if the new solution T is better than the previous best solution Tb (x, y). A possible “bad” candidate can replace the current best due to the Boltzmann probability. A new threshold plane Tb (x, y) is defined which is mapped to the 2D distribution map...

Geometric Optimization of Thermo-electric Coolers Using Simulated Annealing

International Nuclear Information System (INIS)

Khanh, D V K; Vasant, P M; Elamvazuthi, I; Dieu, V N

2015-01-01

The field of thermo-electric coolers (TECs) has grown drastically in recent years. In an extreme environment as thermal energy and gas drilling operations, TEC is an effective cooling mechanism for instrument. However, limitations such as the relatively low energy conversion efficiency and ability to dissipate only a limited amount of heat flux may seriously damage the lifetime and performance of the instrument. Until now, many researches were conducted to expand the efficiency of TECs. The material parameters are the most significant, but they are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of finding the optimal TECs design is to define a set of design parameters. In this paper, a new method of optimizing the dimension of TECs using simulated annealing (SA), to maximize the rate of refrigeration (ROR) was proposed. Equality constraint and inequality constraint were taken into consideration. This work reveals that SA shows better performance than Cheng's work. (paper)

High-temperature annealing of graphite: A molecular dynamics study

Science.gov (United States)

Petersen, Andrew; Gillette, Victor

2018-05-01

A modified AIREBO potential was developed to simulate the effects of thermal annealing on the structure and physical properties of damaged graphite. AIREBO parameter modifications were made to reproduce Density Functional Theory interstitial results. These changes to the potential resulted in high-temperature annealing of the model, as measured by stored-energy reduction. These results show some resemblance to experimental high-temperature annealing results, and show promise that annealing effects in graphite are accessible with molecular dynamics and reactive potentials.

Stochastic-Strength-Based Damage Simulation of Ceramic Matrix Composite Laminates

Science.gov (United States)

Nemeth, Noel N.; Mital, Subodh K.; Murthy, Pappu L. N.; Bednarcyk, Brett A.; Pineda, Evan J.; Bhatt, Ramakrishna T.; Arnold, Steven M.

2016-01-01

The Finite Element Analysis-Micromechanics Analysis Code/Ceramics Analysis and Reliability Evaluation of Structures (FEAMAC/CARES) program was used to characterize and predict the progressive damage response of silicon-carbide-fiber-reinforced reaction-bonded silicon nitride matrix (SiC/RBSN) composite laminate tensile specimens. Studied were unidirectional laminates [0] (sub 8), [10] (sub 8), [45] (sub 8), and [90] (sub 8); cross-ply laminates [0 (sub 2) divided by 90 (sub 2),]s; angled-ply laminates [plus 45 (sub 2) divided by -45 (sub 2), ]s; doubled-edge-notched [0] (sub 8), laminates; and central-hole laminates. Results correlated well with the experimental data. This work was performed as a validation and benchmarking exercise of the FEAMAC/CARES program. FEAMAC/CARES simulates stochastic-based discrete-event progressive damage of ceramic matrix composite and polymer matrix composite material structures. It couples three software programs: (1) the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC), (2) the Ceramics Analysis and Reliability Evaluation of Structures Life Prediction Program (CARES/Life), and (3) the Abaqus finite element analysis program. MAC/GMC contributes multiscale modeling capabilities and micromechanics relations to determine stresses and deformations at the microscale of the composite material repeating-unit-cell (RUC). CARES/Life contributes statistical multiaxial failure criteria that can be applied to the individual brittle-material constituents of the RUC, and Abaqus is used to model the overall composite structure. For each FEAMAC/CARES simulation trial, the stochastic nature of brittle material strength results in random, discrete damage events that incrementally progress until ultimate structural failure.

Stochastic optimization-based study of dimerization kinetics

Indian Academy of Sciences (India)

To this end, we study dimerization kinetics of protein as a model system. We follow the dimerization kinetics using a stochastic simulation algorithm and ... optimization; dimerization kinetics; sensitivity analysis; stochastic simulation ... tion in large molecules and clusters, or the design ..... An unbiased strategy of allocating.

Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays

Energy Technology Data Exchange (ETDEWEB)

Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento (Italy)

2014-10-07

We propose a new exact stochastic rejection-based simulation algorithm for biochemical reactions and extend it to systems with delays. Our algorithm accelerates the simulation by pre-computing reaction propensity bounds to select the next reaction to perform. Exploiting such bounds, we are able to avoid recomputing propensities every time a (delayed) reaction is initiated or finished, as is typically necessary in standard approaches. Propensity updates in our approach are still performed, but only infrequently and limited for a small number of reactions, saving computation time and without sacrificing exactness. We evaluate the performance improvement of our algorithm by experimenting with concrete biological models.

Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods.

Science.gov (United States)

Kucza, Witold

2013-07-25

Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg-Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches. Copyright © 2013 The Author. Published by Elsevier B.V. All rights reserved.

Exploration of DGVM Parameter Solution Space Using Simulated Annealing: Implications for Forecast Uncertainties

Science.gov (United States)

Wells, J. R.; Kim, J. B.

2011-12-01

Parameters in dynamic global vegetation models (DGVMs) are thought to be weakly constrained and can be a significant source of errors and uncertainties. DGVMs use between 5 and 26 plant functional types (PFTs) to represent the average plant life form in each simulated plot, and each PFT typically has a dozen or more parameters that define the way it uses resource and responds to the simulated growing environment. Sensitivity analysis explores how varying parameters affects the output, but does not do a full exploration of the parameter solution space. The solution space for DGVM parameter values are thought to be complex and non-linear; and multiple sets of acceptable parameters may exist. In published studies, PFT parameters are estimated from published literature, and often a parameter value is estimated from a single published value. Further, the parameters are "tuned" using somewhat arbitrary, "trial-and-error" methods. BIOMAP is a new DGVM created by fusing MAPSS biogeography model with Biome-BGC. It represents the vegetation of North America using 26 PFTs. We are using simulated annealing, a global search method, to systematically and objectively explore the solution space for the BIOMAP PFTs and system parameters important for plant water use. We defined the boundaries of the solution space by obtaining maximum and minimum values from published literature, and where those were not available, using +/-20% of current values. We used stratified random sampling to select a set of grid cells representing the vegetation of the conterminous USA. Simulated annealing algorithm is applied to the parameters for spin-up and a transient run during the historical period 1961-1990. A set of parameter values is considered acceptable if the associated simulation run produces a modern potential vegetation distribution map that is as accurate as one produced by trial-and-error calibration. We expect to confirm that the solution space is non-linear and complex, and that

Conventional treatment planning optimization using simulated annealing

International Nuclear Information System (INIS)

Morrill, S.M.; Langer, M.; Lane, R.G.

1995-01-01

Purpose: Simulated annealing (SA) allows for the implementation of realistic biological and clinical cost functions into treatment plan optimization. However, a drawback to the clinical implementation of SA optimization is that large numbers of beams appear in the final solution, some with insignificant weights, preventing the delivery of these optimized plans using conventional (limited to a few coplanar beams) radiation therapy. A preliminary study suggested two promising algorithms for restricting the number of beam weights. The purpose of this investigation was to compare these two algorithms using our current SA algorithm with the aim of producing a algorithm to allow clinically useful radiation therapy treatment planning optimization. Method: Our current SA algorithm, Variable Stepsize Generalized Simulated Annealing (VSGSA) was modified with two algorithms to restrict the number of beam weights in the final solution. The first algorithm selected combinations of a fixed number of beams from the complete solution space at each iterative step of the optimization process. The second reduced the allowed number of beams by a factor of two at periodic steps during the optimization process until only the specified number of beams remained. Results of optimization of beam weights and angles using these algorithms were compared using a standard cadre of abdominal cases. The solution space was defined as a set of 36 custom-shaped open and wedged-filtered fields at 10 deg. increments with a target constant target volume margin of 1.2 cm. For each case a clinically-accepted cost function, minimum tumor dose was maximized subject to a set of normal tissue binary dose-volume constraints. For this study, the optimized plan was restricted to four (4) fields suitable for delivery with conventional therapy equipment. Results: The table gives the mean value of the minimum target dose obtained for each algorithm averaged over 5 different runs and the comparable manual treatment

Stochastic-Strength-Based Damage Simulation Tool for Ceramic Matrix Composite

Science.gov (United States)

Nemeth, Noel; Bednarcyk, Brett; Pineda, Evan; Arnold, Steven; Mital, Subodh; Murthy, Pappu

2015-01-01

Reported here is a coupling of two NASA developed codes: CARES (Ceramics Analysis and Reliability Evaluation of Structures) with the MAC/GMC (Micromechanics Analysis Code/ Generalized Method of Cells) composite material analysis code. The resulting code is called FEAMAC/CARES and is constructed as an Abaqus finite element analysis UMAT (user defined material). Here we describe the FEAMAC/CARES code and an example problem (taken from the open literature) of a laminated CMC in off-axis loading is shown. FEAMAC/CARES performs stochastic-strength-based damage simulation response of a CMC under multiaxial loading using elastic stiffness reduction of the failed elements.

Testing the new stochastic neutronic code ANET in simulating safety important parameters

International Nuclear Information System (INIS)

Xenofontos, T.; Delipei, G.-K.; Savva, P.; Varvayanni, M.; Maillard, J.; Silva, J.; Catsaros, N.

2017-01-01

Highlights: • ANET is a new neutronics stochastic code. • Criticality calculations in both subcritical and critical nuclear systems of conventional design were conducted. • Simulations of thermal, lower epithermal and fast neutron fluence rates were performed. • Axial fission rate distributions in standard and MOX fuel pins were computed. - Abstract: ANET (Advanced Neutronics with Evolution and Thermal hydraulic feedback) is an under development Monte Carlo code for simulating both GEN II/III reactors as well as innovative nuclear reactor designs, based on the high energy physics code GEANT3.21 of CERN. ANET is built through continuous GEANT3.21 applicability amplifications, comprising the simulation of particles’ transport and interaction in low energy along with the accessibility of user-provided libraries and tracking algorithms for energies below 20 MeV, as well as the simulation of elastic and inelastic collision, capture and fission. Successive testing applications performed throughout the ANET development have been utilized to verify the new code capabilities. In this context the ANET reliability in simulating certain reactor parameters important to safety is here examined. More specifically the reactor criticality as well as the neutron fluence and fission rates are benchmarked and validated. The Portuguese Research Reactor (RPI) after its conversion to low enrichment in U-235 and the OECD/NEA VENUS-2 MOX international benchmark were considered appropriate for the present study, the former providing criticality and neutron flux data and the latter reaction rates. Concerning criticality benchmarking, the subcritical, Training Nuclear Reactor of the Aristotle University of Thessaloniki (TNR-AUTh) was also analyzed. The obtained results are compared with experimental data from the critical infrastructures and with computations performed by two different, well established stochastic neutronics codes, i.e. TRIPOLI-4.8 and MCNP5. Satisfactory agreement

Memoryless cooperative graph search based on the simulated annealing algorithm

International Nuclear Information System (INIS)

Hou Jian; Yan Gang-Feng; Fan Zhen

2011-01-01

We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment. (interdisciplinary physics and related areas of science and technology)

Analysis of optimisation method for a two-stroke piston ring using the Finite Element Method and the Simulated Annealing Method

Science.gov (United States)

Kaliszewski, M.; Mazuro, P.

2016-09-01

Simulated Annealing Method of optimisation for the sealing piston ring geometry is tested. The aim of optimisation is to develop ring geometry which would exert demanded pressure on a cylinder just while being bended to fit the cylinder. Method of FEM analysis of an arbitrary piston ring geometry is applied in an ANSYS software. The demanded pressure function (basing on formulae presented by A. Iskra) as well as objective function are introduced. Geometry definition constructed by polynomials in radial coordinate system is delivered and discussed. Possible application of Simulated Annealing Method in a piston ring optimisation task is proposed and visualised. Difficulties leading to possible lack of convergence of optimisation are presented. An example of an unsuccessful optimisation performed in APDL is discussed. Possible line of further optimisation improvement is proposed.

Optimising Shovel-Truck Fuel Consumption using Stochastic ...

African Journals Online (AJOL)

Optimising the fuel consumption and truck waiting time can result in significant fuel savings. The paper demonstrates that stochastic simulation is an effective tool for optimising the utilisation of fossil-based fuels in mining and related industries. Keywords: Stochastic, Simulation Modelling, Mining, Optimisation, Shovel-Truck ...

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

A dynamic programming–enhanced simulated annealing algorithm for solving bi-objective cell formation problem with duplicate machines

Directory of Open Access Journals (Sweden)

Mohammad Mohammadi

2015-04-01

Full Text Available Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a bi-objective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality.

Optimization of permanent-magnet undulator magnets ordering using simulated annealing algorithm

International Nuclear Information System (INIS)

Chen Nian; He Duohui; Li Ge; Jia Qika; Zhang Pengfei; Xu Hongliang; Cai Genwang

2005-01-01

Pure permanent-magnet undulator consists of many magnets. The unavoidable remanence divergence of these magnets causes the undulator magnetic field error, which will affect the functional mode of the storage ring and the quality of the spontaneous emission spectrum. Optimizing permanent-magnet undulator magnets ordering using simulated annealing algorithm before installing undulator magnets, the first field integral can be reduced to 10 -6 T·m, the second integral to 10 -6 T·m 2 and the peak field error to less than 10 -4 . The optimized results are independent of the initial solution. This paper gives the optimizing process in detail and puts forward a method to quickly calculate the peak field error and field integral according to the magnet remanence. (authors)

Application of users’ light-switch stochastic models to dynamic energy simulation

DEFF Research Database (Denmark)

Camisassi, V.; Fabi, V.; Andersen, Rune Korsholm

2015-01-01

deterministic inputs, due to the uncertain nature of human behaviour. In this paper, new stochastic models of users’ interaction with artificial lighting systems are developed and implemented in the energy simulation software IDA ICE. They were developed from field measurements in an office building in Prague......The design of an innovative building should include building overall energy flows estimation. They are principally related to main six influencing factors (IEA-ECB Annex 53): climate, building envelope and equipment, operation and maintenance, occupant behaviour and indoor environment conditions...

Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration

Energy Technology Data Exchange (ETDEWEB)

Sun, Kaiyu; Yan, Da; Hong, Tianzhen; Guo, Siyue

2014-02-28

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

PedMine – A simulated annealing algorithm to identify maximally unrelated individuals in population isolates

OpenAIRE

Douglas, Julie A.; Sandefur, Conner I.

2008-01-01

In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a ge...

Stochastic simulation modeling to determine time to detect Bovine Viral Diarrhea antibodies in bulk tank milk

DEFF Research Database (Denmark)

Foddai, Alessandro; Enøe, Claes; Krogh, Kaspar

2014-01-01

A stochastic simulation model was developed to estimate the time from introduction ofBovine Viral Diarrhea Virus (BVDV) in a herd to detection of antibodies in bulk tank milk(BTM) samples using three ELISAs. We assumed that antibodies could be detected, after afixed threshold prevalence of seroco......A stochastic simulation model was developed to estimate the time from introduction ofBovine Viral Diarrhea Virus (BVDV) in a herd to detection of antibodies in bulk tank milk(BTM) samples using three ELISAs. We assumed that antibodies could be detected, after afixed threshold prevalence......, which was the most efficient ELISA, could detect antibodiesin the BTM of a large herd 280 days (95% prediction interval: 218; 568) after a transientlyinfected (TI) milking cow has been introduced into the herd. The estimated time to detectionafter introduction of one PI calf was 111 days (44; 605...

Identification of exploration strategies for electric power distribution network using simulated annealing; Identificao de estrategias de exploracao de redes de distribuicao de energia electrica utilizando simulated annealing

Energy Technology Data Exchange (ETDEWEB)

Pereira, Jorge; Saraiva, J. Tome; Leao, Maria Teresa Ponce de [Instituto de Engenharia de Sistemas e Computadores (INESC), Porto (Portugal). E-mail: jpereira@inescn.pt; jsaraiva@inescn.pt; mleao@inescn.pt

1999-07-01

This paper presents a model for identification of optimum strategies for electric power distribution networks, considering the aim of minimizing the active power losses. This objective can be attained by modifying the transformer connections or modification of the condenser groups on duty. By the other side, specifications of voltage ranges for each bar and current intensity limits for the branches are admitted, in order to obtain a more realistic the used model. The paper describes the the simulated annealing in order to surpass the mentioned difficulties. The application of the method to the problem resolution allows the identification solutions based on exact models. The application is illustrated with the results obtained by using a IEEE test network and a network based on real distribution with 645 bars.

2–stage stochastic Runge–Kutta for stochastic delay differential equations

Energy Technology Data Exchange (ETDEWEB)

Rosli, Norhayati; Jusoh Awang, Rahimah [Faculty of Industrial Science and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Gambang, Pahang (Malaysia); Bahar, Arifah; Yeak, S. H. [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

2015-05-15

This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. General formulation of stochastic Runge-Kutta for SDDEs is introduced and Stratonovich Taylor series expansion for numerical solution of SRK2 is presented. Local truncation error of SRK2 is measured by comparing the Stratonovich Taylor expansion of the exact solution with the computed solution. Numerical experiment is performed to assure the validity of the method in simulating the strong solution of SDDEs.

Simulating local measurements on a quantum many-body system with stochastic matrix product states

DEFF Research Database (Denmark)

Gammelmark, Søren; Mølmer, Klaus

2010-01-01

We demonstrate how to simulate both discrete and continuous stochastic evolutions of a quantum many-body system subject to measurements using matrix product states. A particular, but generally applicable, measurement model is analyzed and a simple representation in terms of matrix product operators...... is found. The technique is exemplified by numerical simulations of the antiferromagnetic Heisenberg spin-chain model subject to various instances of the measurement model. In particular, we focus on local measurements with small support and nonlocal measurements, which induce long-range correlations....

Stochastic simulation and decadal prediction of hydroclimate in the Western Himalayas

Science.gov (United States)

Robertson, A. W.; Chekroun, M. D.; Cook, E.; D'Arrigo, R.; Ghil, M.; Greene, A. M.; Holsclaw, T.; Kondrashov, D. A.; Lall, U.; Lu, M.; Smyth, P.

2012-12-01

Improved estimates of climate over the next 10 to 50 years are needed for long-term planning in water resource and flood management. However, the task of effectively incorporating the results of climate change research into decision-making face a ``double conflict of scales'': the temporal scales of climate model projections are too long, while their usable spatial scales (global to planetary) are much larger than those needed for actual decision making (at the regional to local level). This work is designed to help tackle this ``double conflict'' in the context of water management over monsoonal Asia, based on dendroclimatic multi-century reconstructions of drought indices and river flows. We identify low-frequency modes of variability with time scales from interannual to interdecadal based on these series, and then generate future scenarios based on (a) empirical model decadal predictions, and (b) stochastic simulations generated with autoregressive models that reproduce the power spectrum of the data. Finally, we consider how such scenarios could be used to develop reservoir optimization models. Results will be presented based on multi-century Upper Indus river discharge reconstructions that exhibit a strong periodicity near 27 years that is shown to yield some retrospective forecasting skill over the 1700-2000 period, at a 15-yr yield time. Stochastic simulations of annual PDSI drought index values over the Upper Indus basin are constructed using Empirical Model Reduction; their power spectra are shown to be quite realistic, with spectral peaks near 5--8 years.

Stochastic Analysis with Financial Applications

CERN Document Server

Kohatsu-Higa, Arturo; Sheu, Shuenn-Jyi

2011-01-01

Stochastic analysis has a variety of applications to biological systems as well as physical and engineering problems, and its applications to finance and insurance have bloomed exponentially in recent times. The goal of this book is to present a broad overview of the range of applications of stochastic analysis and some of its recent theoretical developments. This includes numerical simulation, error analysis, parameter estimation, as well as control and robustness properties for stochastic equations. This book also covers the areas of backward stochastic differential equations via the (non-li

The performance of simulated annealing in parameter estimation for vapor-liquid equilibrium modeling

Directory of Open Access Journals (Sweden)

A. Bonilla-Petriciolet

2007-03-01

Full Text Available In this paper we report the application and evaluation of the simulated annealing (SA optimization method in parameter estimation for vapor-liquid equilibrium (VLE modeling. We tested this optimization method using the classical least squares and error-in-variable approaches. The reliability and efficiency of the data-fitting procedure are also considered using different values for algorithm parameters of the SA method. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. However, in difficult problems it still can converge to local optimums of the objective function.

Design and validation of a dynamic discrete event stochastic simulation model of mastitis control in dairy herds.

Science.gov (United States)

Allore, H G; Schruben, L W; Erb, H N; Oltenacu, P A

1998-03-01

A dynamic stochastic simulation model for discrete events, SIMMAST, was developed to simulate the effect of mastitis on the composition of the bulk tank milk of dairy herds. Intramammary infections caused by Streptococcus agalactiae, Streptococcus spp. other than Strep. agalactiae, Staphylococcus aureus, and coagulase-negative staphylococci were modeled as were the milk, fat, and protein test day solutions for individual cows, which accounted for the fixed effects of days in milk, age at calving, season of calving, somatic cell count (SCC), and random effects of test day, cow yield differences from herdmates, and autocorrelated errors. Probabilities for the transitions among various states of udder health (uninfected or subclinically or clinically infected) were calculated to account for exposure, heifer infection, spontaneous recovery, lactation cure, infection or cure during the dry period, month of lactation, parity, within-herd yields, and the number of quarters with clinical intramammary infection in the previous and current lactations. The stochastic simulation model was constructed using estimates from the literature and also using data from 164 herds enrolled with Quality Milk Promotion Services that each had bulk tank SCC between 500,000 and 750,000/ml. Model parameters and outputs were validated against a separate data file of 69 herds from the Northeast Dairy Herd Improvement Association, each with a bulk tank SCC that was > or = 500,000/ml. Sensitivity analysis was performed on all input parameters for control herds. Using the validated stochastic simulation model, the control herds had a stable time average bulk tank SCC between 500,000 and 750,000/ml.

Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow

Science.gov (United States)

Kolars, Kelsey A.; Vecchia, Aldo V.; Ryberg, Karen R.

2016-02-24

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses.Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states.A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state.A water

Experiments and stochastic simulations of lignite coal during pyrolysis and gasification

International Nuclear Information System (INIS)

Ahmed, I.I.; Gupta, A.K.

2013-01-01

Highlights: ► Lignite pyrolysis and gasification has been conducted in a semi batch reactor. ► The objective is to understand mechanism of syngas evolution during pyrolysis. ► Stochastic simulations of lignite pyrolysis were conducted using Gillespie algorithm. ► First order, single step mechanism failed to fit cumulative yield of hydrogen. ► Evolution of hydrogen via pyrolysis of gaseous hydrocarbon following bridges scission. -- Abstract: Lignite pyrolysis and gasification has been conducted in a semi batch reactor at reactor temperatures of 800–950 °C in 50 °C intervals. CO 2 has been used as the gasifying agent for gasification experiments. The objective of this investigation is to understand the mechanism of syngas evolution during pyrolysis and to unravel the effect of CO 2 on pyrolysis mechanism. Stochastic simulations of lignite pyrolysis have been conducted using Gillespie algorithm. Two reaction mechanisms have been used in the simulations; first order, single step mechanism and the FLASHCHAIN mechanism. The first order single step mechanism was successful in fitting cumulative yield of CO 2 , CO, CH 4 and other hydrocarbons (C n H m ). The first order, single step failed to fit the cumulative yield of hydrogen, which suggests a more complex mechanism for hydrogen evolution. Evolution of CO 2 , CO, CH 4 , C n H m and H 2 flow rates has been monitored. The only effect of CO 2 on pyrolysis mechanism is promotion of reverse water gas shift reaction for the experiments described here. Methane evolution extended for slightly longer time than other hydrocarbons and hydrogen evolution extended for a slightly longer time than methane. This indicated the evolution of hydrogen via further pyrolysis of aliphatic hydrocarbon. It is also suggested that this step occurs in series after aliphatic hydrocarbons evolution by bridges scission.

Numerical simulations of piecewise deterministic Markov processes with an application to the stochastic Hodgkin-Huxley model

Science.gov (United States)

Ding, Shaojie; Qian, Min; Qian, Hong; Zhang, Xuejuan

2016-12-01

The stochastic Hodgkin-Huxley model is one of the best-known examples of piecewise deterministic Markov processes (PDMPs), in which the electrical potential across a cell membrane, V(t), is coupled with a mesoscopic Markov jump process representing the stochastic opening and closing of ion channels embedded in the membrane. The rates of the channel kinetics, in turn, are voltage-dependent. Due to this interdependence, an accurate and efficient sampling of the time evolution of the hybrid stochastic systems has been challenging. The current exact simulation methods require solving a voltage-dependent hitting time problem for multiple path-dependent intensity functions with random thresholds. This paper proposes a simulation algorithm that approximates an alternative representation of the exact solution by fitting the log-survival function of the inter-jump dwell time, H(t), with a piecewise linear one. The latter uses interpolation points that are chosen according to the time evolution of the H(t), as the numerical solution to the coupled ordinary differential equations of V(t) and H(t). This computational method can be applied to all PDMPs. Pathwise convergence of the approximated sample trajectories to the exact solution is proven, and error estimates are provided. Comparison with a previous algorithm that is based on piecewise constant approximation is also presented.

Application of simulated annealing in simulation and optimization of drying process of Zea mays malt Aplicação do simulated annealing na simulação e otimização do processo de secagem do malte de Zea mays

Directory of Open Access Journals (Sweden)

Marco A. C. Benvenga

2011-10-01

Full Text Available Kinetic simulation and drying process optimization of corn malt by Simulated Annealing (SA for estimation of temperature and time parameters in order to preserve maximum amylase activity in the obtained product are presented here. Germinated corn seeds were dried at 54-76 °C in a convective dryer, with occasional measurement of moisture content and enzymatic activity. The experimental data obtained were submitted to modeling. Simulation and optimization of the drying process were made by using the SA method, a randomized improvement algorithm, analogous to the simulated annealing process. Results showed that seeds were best dried between 3h and 5h. Among the models used in this work, the kinetic model of water diffusion into corn seeds showed the best fitting. Drying temperature and time showed a square influence on the enzymatic activity. Optimization through SA showed the best condition at 54 ºC and between 5.6h and 6.4h of drying. Values of specific activity in the corn malt were found between 5.26±0.06 SKB/mg and 15.69±0,10% of remaining moisture.Este trabalho objetivou a simulação da cinética e a otimização do processo de secagem do malte de milho por meio da técnica Simulated Annealing (SA, para estimação dos parâmetros de temperatura e tempo, tais que mantenham a atividade máxima das enzimas amilases no produto obtido. Para tanto, as sementes de milho germinadas foram secas entre 54-76°C, em um secador convectivo de ar. De tempo em tempo, a umidade e a atividade enzimática foram medidas. Esses dados experimentais foram usados para testar os modelos. A simulação e a otimização do processo foram feitas por meio do método SA, um algoritmo de melhoria randômica, análogo ao processo de têmpera simulada. Os resultados mostram que as sementes estavam secas após 3 h ou 5 h de secagem. Entre os modelos usados, o modelo cinético de difusão da água através das sementes apresentou o melhor ajuste. O tempo e a temperatura

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

Science.gov (United States)

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Stochastic-shielding approximation of Markov chains and its application to efficiently simulate random ion-channel gating.

Science.gov (United States)

Schmandt, Nicolaus T; Galán, Roberto F

2012-09-14

Markov chains provide realistic models of numerous stochastic processes in nature. We demonstrate that in any Markov chain, the change in occupation number in state A is correlated to the change in occupation number in state B if and only if A and B are directly connected. This implies that if we are only interested in state A, fluctuations in B may be replaced with their mean if state B is not directly connected to A, which shortens computing time considerably. We show the accuracy and efficacy of our approximation theoretically and in simulations of stochastic ion-channel gating in neurons.

Stochastic strong ground motion simulations for the intermediate-depth earthquakes of the south Aegean subduction zone

Science.gov (United States)

Kkallas, Harris; Papazachos, Konstantinos; Boore, David; Margaris, Vasilis

2015-04-01

We have employed the stochastic finite-fault modelling approach of Motazedian and Atkinson (2005), as described by Boore (2009), for the simulation of Fourier spectra of the Intermediate-depth earthquakes of the south Aegean subduction zone. The stochastic finite-fault method is a practical tool for simulating ground motions of future earthquakes which requires region-specific source, path and site characterizations as input model parameters. For this reason we have used data from both acceleration-sensor and broadband velocity-sensor instruments from intermediate-depth earthquakes with magnitude of M 4.5-6.7 that occurred in the south Aegean subduction zone. Source mechanisms for intermediate-depth events of north Aegean subduction zone are either collected from published information or are constrained using the main faulting types from Kkallas et al. (2013). The attenuation parameters for simulations were adopted from Skarladoudis et al. (2013) and are based on regression analysis of a response spectra database. The site amplification functions for each soil class were adopted from Klimis et al., (1999), while the kappa values were constrained from the analysis of the EGELADOS network data from Ventouzi et al., (2013). The investigation of stress-drop values was based on simulations performed with the EXSIM code for several ranges of stress drop values and by comparing the results with the available Fourier spectra of intermediate-depth earthquakes. Significant differences regarding the strong-motion duration, which is determined from Husid plots (Husid, 1969), have been identified between the for-arc and along-arc stations due to the effect of the low-velocity/low-Q mantle wedge on the seismic wave propagation. In order to estimate appropriate values for the duration of P-waves, we have automatically picked P-S durations on the available seismograms. For the S-wave durations we have used the part of the seismograms starting from the S-arrivals and ending at the

Stochastic porous media modeling and high-resolution schemes for numerical simulation of subsurface immiscible fluid flow transport

Science.gov (United States)

Brantson, Eric Thompson; Ju, Binshan; Wu, Dan; Gyan, Patricia Semwaah

2018-04-01

This paper proposes stochastic petroleum porous media modeling for immiscible fluid flow simulation using Dykstra-Parson coefficient (V DP) and autocorrelation lengths to generate 2D stochastic permeability values which were also used to generate porosity fields through a linear interpolation technique based on Carman-Kozeny equation. The proposed method of permeability field generation in this study was compared to turning bands method (TBM) and uniform sampling randomization method (USRM). On the other hand, many studies have also reported that, upstream mobility weighting schemes, commonly used in conventional numerical reservoir simulators do not accurately capture immiscible displacement shocks and discontinuities through stochastically generated porous media. This can be attributed to high level of numerical smearing in first-order schemes, oftentimes misinterpreted as subsurface geological features. Therefore, this work employs high-resolution schemes of SUPERBEE flux limiter, weighted essentially non-oscillatory scheme (WENO), and monotone upstream-centered schemes for conservation laws (MUSCL) to accurately capture immiscible fluid flow transport in stochastic porous media. The high-order schemes results match well with Buckley Leverett (BL) analytical solution without any non-oscillatory solutions. The governing fluid flow equations were solved numerically using simultaneous solution (SS) technique, sequential solution (SEQ) technique and iterative implicit pressure and explicit saturation (IMPES) technique which produce acceptable numerical stability and convergence rate. A comparative and numerical examples study of flow transport through the proposed method, TBM and USRM permeability fields revealed detailed subsurface instabilities with their corresponding ultimate recovery factors. Also, the impact of autocorrelation lengths on immiscible fluid flow transport were analyzed and quantified. A finite number of lines used in the TBM resulted into visual

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...

Stochastic simulation of destruction processes in self-irradiated materials

Directory of Open Access Journals (Sweden)

T. Patsahan

2017-09-01

Full Text Available Self-irradiation damages resulting from fission processes are common phenomena observed in nuclear fuel containing (NFC materials. Numerous α-decays lead to local structure transformations in NFC materials. The damages appearing due to the impacts of heavy nuclear recoils in the subsurface layer can cause detachments of material particles. Such a behaviour is similar to sputtering processes observed during a bombardment of the material surface by a flux of energetic particles. However, in the NFC material, the impacts are initiated from the bulk. In this work we propose a two-dimensional mesoscopic model to perform a stochastic simulation of the destruction processes occurring in a subsurface region of NFC material. We describe the erosion of the material surface, the evolution of its roughness and predict the detachment of the material particles. Size distributions of the emitted particles are obtained in this study. The simulation results of the model are in a qualitative agreement with the size histogram of particles produced from the material containing lava-like fuel formed during the Chernobyl nuclear power plant disaster.

Experiences using DAKOTA stochastic expansion methods in computational simulations.

Energy Technology Data Exchange (ETDEWEB)

Templeton, Jeremy Alan; Ruthruff, Joseph R.

2012-01-01

Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of computational and experiment data, and provide a basis for probabilistically assessing margins associated with safety and reliability. The DAKOTA toolkit developed at Sandia National Laboratories implements a number of UQ methods, which are being increasingly adopted by modeling and simulation teams to facilitate these analyses. This report disseminates results as to the performance of DAKOTA's stochastic expansion methods for UQ on a representative application. Our results provide a number of insights that may be of interest to future users of these methods, including the behavior of the methods in estimating responses at varying probability levels, and the expansion levels for the methodologies that may be needed to achieve convergence.

Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

International Nuclear Information System (INIS)

Sigala, R; Smerieri, A; Camorani, P; Schüz, A; Erokhin, V

2013-01-01

Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)

1-Dimensional simulation of thermal annealing in a commercial nuclear power plant reactor pressure vessel wall section

International Nuclear Information System (INIS)

Nakos, J.T.; Rosinski, S.T.; Acton, R.U.

1994-11-01

The objective of this work was to provide experimental heat transfer boundary condition and reactor pressure vessel (RPV) section thermal response data that can be used to benchmark computer codes that simulate thermal annealing of RPVS. This specific protect was designed to provide the Electric Power Research Institute (EPRI) with experimental data that could be used to support the development of a thermal annealing model. A secondary benefit is to provide additional experimental data (e.g., thermal response of concrete reactor cavity wall) that could be of use in an annealing demonstration project. The setup comprised a heater assembly, a 1.2 in x 1.2 m x 17.1 cm thick [4 ft x 4 ft x 6.75 in] section of an RPV (A533B ferritic steel with stainless steel cladding), a mockup of the open-quotes mirrorclose quotes insulation between the RPV and the concrete reactor cavity wall, and a 25.4 cm [10 in] thick concrete wall, 2.1 in x 2.1 in [10 ft x 10 ft] square. Experiments were performed at temperature heat-up/cooldown rates of 7, 14, and 28 degrees C/hr [12.5, 25, and 50 degrees F/hr] as measured on the heated face. A peak temperature of 454 degrees C [850 degrees F] was maintained on the heated face until the concrete wall temperature reached equilibrium. Results are most representative of those RPV locations where the heat transfer would be 1-dimensional. Temperature was measured at multiple locations on the heated and unheated faces of the RPV section and the concrete wall. Incident heat flux was measured on the heated face, and absorbed heat flux estimates were generated from temperature measurements and an inverse heat conduction code. Through-wall temperature differences, concrete wall temperature response, heat flux absorbed into the RPV surface and incident on the surface are presented. All of these data are useful to modelers developing codes to simulate RPV annealing

Meta-stochastic simulation of biochemical models for systems and synthetic biology.

Science.gov (United States)

Sanassy, Daven; Widera, Paweł; Krasnogor, Natalio

2015-01-16

Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochemical systems at low species concentrations. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to determine which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochemical model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochemical model with the predicted fastest algorithm as the simulation engine. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.

Annealing effect on thermodynamic and physical properties of mesoporous silicon: A simulation and nitrogen sorption study

Science.gov (United States)

Kumar, Pushpendra; Huber, Patrick

2016-04-01

Discovery of porous silicon formation in silicon substrate in 1956 while electro-polishing crystalline Si in hydrofluoric acid (HF), has triggered large scale investigations of porous silicon formation and their changes in physical and chemical properties with thermal and chemical treatment. A nitrogen sorption study is used to investigate the effect of thermal annealing on electrochemically etched mesoporous silicon (PS). The PS was thermally annealed from 200˚C to 800˚C for 1 hr in the presence of air. It was shown that the pore diameter and porosity of PS vary with annealing temperature. The experimentally obtained adsorption / desorption isotherms show hysteresis typical for capillary condensation in porous materials. A simulation study based on Saam and Cole model was performed and compared with experimentally observed sorption isotherms to study the physics behind of hysteresis formation. We discuss the shape of the hysteresis loops in the framework of the morphology of the layers. The different behavior of adsorption and desorption of nitrogen in PS with pore diameter was discussed in terms of concave menisci formation inside the pore space, which was shown to related with the induced pressure in varying the pore diameter from 7.2 nm to 3.4 nm.

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

Directory of Open Access Journals (Sweden)

Anastasia S. Georgiou

2017-06-01

Full Text Available In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves. Constructing such an exploration geometry involves geometry-biased sampling (through both appropriately-initialized unbiased molecular dynamics and through restraining potentials and, machine learning techniques to organize the intrinsic geometry of the data resulting from the sampling (in particular, diffusion maps, possibly enhanced through the appropriate Mahalanobis-type metric. In this contribution, we detail a method for exploring the conformational space of a stochastic gradient system whose effective free energy surface depends on a smaller number of degrees of freedom than the dimension of the phase space. Our approach comprises two steps. First, we study the local geometry of the free energy landscape using diffusion maps on samples computed through stochastic dynamics. This allows us to automatically identify the relevant coarse variables. Next, we use the information garnered in the previous step to construct a new set of initial conditions for subsequent trajectories. These initial conditions are computed so as to explore the accessible conformational space more efficiently than by continuing the previous, unbiased simulations. We showcase this method on a representative test system.

Stochastic processes and quantum theory

International Nuclear Information System (INIS)

Klauder, J.R.

1975-01-01

The author analyses a variety of stochastic processes, namely real time diffusion phenomena, which are analogues of imaginary time quantum theory and convariant imaginary time quantum field theory. He elaborates some standard properties involving probability measures and stochastic variables and considers a simple class of examples. Finally he develops the fact that certain stochastic theories actually exhibit divergences that simulate those of covariant quantum field theory and presents examples of both renormaizable and unrenormalizable behavior. (V.J.C.)

A stochastic model for simulation of the economic consequences of bovine virus diarrhoea virus infection in a dairy herd

DEFF Research Database (Denmark)

Sørensen, J.T.; Enevoldsen, Carsten; Houe, H.

1995-01-01

A dynamic, stochastic model simulating the technical and economic consequences of bovine virus diarrhoea virus (BVDV) infections for a dairy cattle herd for use on a personal computer was developed. The production and state changes of the herd were simulated by state changes of the individual cows...... and heifers. All discrete events at the cow level were triggered stochastically. Each cow and heifer was characterized by state variables such as stage of lactation, parity, oestrous status, decision for culling, milk production potential, and immune status for BVDV. The model was controlled by 170 decision...... variables describing biologic and management variables including 21 decision variables describing the effect of BVDV infection on the production of the individual animal. Two markedly different scenarios were simulated to demonstrate the behaviour of the developed model and the potentials of the applied...

Cyto-Sim: a formal language model and stochastic simulator of membrane-enclosed biochemical processes.

Science.gov (United States)

Sedwards, Sean; Mazza, Tommaso

2007-10-15

Compartments and membranes are the basis of cell topology and more than 30% of the human genome codes for membrane proteins. While it is possible to represent compartments and membrane proteins in a nominal way with many mathematical formalisms used in systems biology, few, if any, explicitly model the topology of the membranes themselves. Discrete stochastic simulation potentially offers the most accurate representation of cell dynamics. Since the details of every molecular interaction in a pathway are often not known, the relationship between chemical species in not necessarily best described at the lowest level, i.e. by mass action. Simulation is a form of computer-aided analysis, relying on human interpretation to derive meaning. To improve efficiency and gain meaning in an automatic way, it is necessary to have a formalism based on a model which has decidable properties. We present Cyto-Sim, a stochastic simulator of membrane-enclosed hierarchies of biochemical processes, where the membranes comprise an inner, outer and integral layer. The underlying model is based on formal language theory and has been shown to have decidable properties (Cavaliere and Sedwards, 2006), allowing formal analysis in addition to simulation. The simulator provides variable levels of abstraction via arbitrary chemical kinetics which link to ordinary differential equations. In addition to its compact native syntax, Cyto-Sim currently supports models described as Petri nets, can import all versions of SBML and can export SBML and MATLAB m-files. Cyto-Sim is available free, either as an applet or a stand-alone Java program via the web page (http://www.cosbi.eu/Rpty_Soft_CytoSim.php). Other versions can be made available upon request.

Protein structure predictions with Monte Carlo simulated annealing: Case for the β-sheet

Science.gov (United States)

Okamoto, Y.; Fukugita, M.; Kawai, H.; Nakazawa, T.

Work is continued for a prediction of three-dimensional structure of peptides and proteins with Monte Carlo simulated annealing using only a generic energy function and amino acid sequence as input. We report that β-sheet like structure is successfully predicted for a fragment of bovine pancreatic trypsin inhibitor which is known to have the β-sheet structure in nature. Together with the results for α-helix structure reported earlier, this means that a successful prediction can be made, at least at a qualitative level, for two dominant building blocks of proteins, α-helix and β-sheet, from the information of amino acid sequence alone.

Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation

NARCIS (Netherlands)

Bewley, J.M.; Boehlje, M.D.; Gray, A.W.; Hogeveen, H.; Kenyon, S.J.; Eicher, S.D.; Schutz, M.M.

2010-01-01

Purpose – The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm

Solving Langevin equation with the stochastic algebraically correlated noise

International Nuclear Information System (INIS)

Ploszajczak, M.; Srokowski, T.

1996-01-01

Long time tail in the velocity and force autocorrelation function has been found recently in the molecular dynamics simulations of the peripheral collisions of ions. Simulation of those slowly decaying correlations in the stochastic transport theory requires the development of new methods of generating stochastic force of arbitrarily long correlation times. The Markovian process and the multidimensional Kangaroo process which permit describing various algebraic correlated stochastic processes are proposed. (author)

Product Costing in FMT: Comparing Deterministic and Stochastic Models Using Computer-Based Simulation for an Actual Case Study

DEFF Research Database (Denmark)

Nielsen, Steen

2000-01-01

This paper expands the traditional product costing technique be including a stochastic form in a complex production process for product costing. The stochastic phenomenon in flesbile manufacturing technologies is seen as an important phenomenon that companies try to decreas og eliminate. DFM has...... been used for evaluating the appropriateness of the firm's production capability. In this paper a simulation model is developed to analyze the relevant cost behaviour with respect to DFM and to develop a more streamlined process in the layout of the manufacturing process....

Stochastic simulation and robust design optimization of integrated photonic filters

Directory of Open Access Journals (Sweden)

Weng Tsui-Wei

2016-07-01

Full Text Available Manufacturing variations are becoming an unavoidable issue in modern fabrication processes; therefore, it is crucial to be able to include stochastic uncertainties in the design phase. In this paper, integrated photonic coupled ring resonator filters are considered as an example of significant interest. The sparsity structure in photonic circuits is exploited to construct a sparse combined generalized polynomial chaos model, which is then used to analyze related statistics and perform robust design optimization. Simulation results show that the optimized circuits are more robust to fabrication process variations and achieve a reduction of 11%–35% in the mean square errors of the 3 dB bandwidth compared to unoptimized nominal designs.

Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation

Directory of Open Access Journals (Sweden)

Mark D McDonnell

2013-05-01

Full Text Available The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of action-potential arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic action potential, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.

Vehicle Routing Problems with Fuel Consumption and Stochastic Travel Speeds

Directory of Open Access Journals (Sweden)

Yanling Feng

2017-01-01

Full Text Available Conventional vehicle routing problems (VRP always assume that the vehicle travel speed is fixed or time-dependent on arcs. However, due to the uncertainty of weather, traffic conditions, and other random factors, it is not appropriate to set travel speeds to fixed constants in advance. Consequently, we propose a mathematic model for calculating expected fuel consumption and fixed vehicle cost where average speed is assumed to obey normal distribution on each arc which is more realistic than the existing model. For small-scaled problems, we make a linear transformation and solve them by existing solver CPLEX, while, for large-scaled problems, an improved simulated annealing (ISA algorithm is constructed. Finally, instances from real road networks of England are performed with the ISA algorithm. Computational results show that our ISA algorithm performs well in a reasonable amount of time. We also find that when taking stochastic speeds into consideration, the fuel consumption is always larger than that with fixed speed model.

Stochastic simulations of normal aging and Werner's syndrome.

KAUST Repository

Qi, Qi

2014-04-26

Human cells typically consist of 23 pairs of chromosomes. Telomeres are repetitive sequences of DNA located at the ends of chromosomes. During cell replication, a number of basepairs are lost from the end of the chromosome and this shortening restricts the number of divisions that a cell can complete before it becomes senescent, or non-replicative. In this paper, we use Monte Carlo simulations to form a stochastic model of telomere shortening to investigate how telomere shortening affects normal aging. Using this model, we study various hypotheses for the way in which shortening occurs by comparing their impact on aging at the chromosome and cell levels. We consider different types of length-dependent loss and replication probabilities to describe these processes. After analyzing a simple model for a population of independent chromosomes, we simulate a population of cells in which each cell has 46 chromosomes and the shortest telomere governs the replicative potential of the cell. We generalize these simulations to Werner\\'s syndrome, a condition in which large sections of DNA are removed during cell division and, amongst other conditions, results in rapid aging. Since the mechanisms governing the loss of additional basepairs are not known, we use our model to simulate a variety of possible forms for the rate at which additional telomeres are lost per replication and several expressions for how the probability of cell division depends on telomere length. As well as the evolution of the mean telomere length, we consider the standard deviation and the shape of the distribution. We compare our results with a variety of data from the literature, covering both experimental data and previous models. We find good agreement for the evolution of telomere length when plotted against population doubling.

Stochastic synaptic plasticity with memristor crossbar arrays

KAUST Repository

Naous, Rawan

2016-11-01

Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.

Stochastic synaptic plasticity with memristor crossbar arrays

KAUST Repository

Naous, Rawan; Al-Shedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2016-01-01

Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.

Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton

International Nuclear Information System (INIS)

Babykina, Génia; Brînzei, Nicolae; Aubry, Jean-François; Deleuze, Gilles

2016-01-01

The paper proposes a modeling framework to support Monte Carlo simulations of the behavior of a complex industrial system. The aim is to analyze the system dependability in the presence of random events, described by any type of probability distributions. Continuous dynamic evolutions of physical parameters are taken into account by a system of differential equations. Dynamic reliability is chosen as theoretical framework. Based on finite state automata theory, the formal model is built by parallel composition of elementary sub-models using a bottom-up approach. Considerations of a stochastic nature lead to a model called the Stochastic Hybrid Automaton. The Scilab/Scicos open source environment is used for implementation. The case study is carried out on an example of a steam generator of a nuclear power plant. The behavior of the system is studied by exploring its trajectories. Possible system trajectories are analyzed both empirically, using the results of Monte Carlo simulations, and analytically, using the formal system model. The obtained results are show to be relevant. The Stochastic Hybrid Automaton appears to be a suitable tool to address the dynamic reliability problem and to model real systems of high complexity; the bottom-up design provides precision and coherency of the system model. - Highlights: • A part of a nuclear power plant is modeled in the context of dynamic reliability. • Stochastic Hybrid Automaton is used as an input model for Monte Carlo simulations. • The model is formally built using a bottom-up approach. • The behavior of the system is analyzed empirically and analytically. • A formally built SHA shows to be a suitable tool to approach dynamic reliability.

An evolutionary programming based simulated annealing method for solving the unit commitment problem

Energy Technology Data Exchange (ETDEWEB)

Christober Asir Rajan, C. [Department of EEE, Pondicherry Engineering College, Pondicherry 605014 (India); Mohan, M.R. [Department of EEE, Anna University, Chennai 600 025 (India)

2007-09-15

This paper presents a new approach to solve the short-term unit commitment problem using an evolutionary programming based simulated annealing method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimisation technique for solving unit commitment Problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status (''flat start''). Here the parents are obtained from a pre-defined set of solution's, i.e. each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit's minimum down times. And SA improves the status. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consists of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the Evolutionary Programming method and other conventional methods like Dynamic Programming, Lagrangian Relaxation and Simulated Annealing and Tabu Search in reaching proper unit commitment. (author)

Stochastic-Strength-Based Damage Simulation Tool for Ceramic Matrix and Polymer Matrix Composite Structures

Science.gov (United States)

Nemeth, Noel N.; Bednarcyk, Brett A.; Pineda, Evan J.; Walton, Owen J.; Arnold, Steven M.

2016-01-01

Stochastic-based, discrete-event progressive damage simulations of ceramic-matrix composite and polymer matrix composite material structures have been enabled through the development of a unique multiscale modeling tool. This effort involves coupling three independently developed software programs: (1) the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC), (2) the Ceramics Analysis and Reliability Evaluation of Structures Life Prediction Program (CARES/ Life), and (3) the Abaqus finite element analysis (FEA) program. MAC/GMC contributes multiscale modeling capabilities and micromechanics relations to determine stresses and deformations at the microscale of the composite material repeating unit cell (RUC). CARES/Life contributes statistical multiaxial failure criteria that can be applied to the individual brittle-material constituents of the RUC. Abaqus is used at the global scale to model the overall composite structure. An Abaqus user-defined material (UMAT) interface, referred to here as "FEAMAC/CARES," was developed that enables MAC/GMC and CARES/Life to operate seamlessly with the Abaqus FEA code. For each FEAMAC/CARES simulation trial, the stochastic nature of brittle material strength results in random, discrete damage events, which incrementally progress and lead to ultimate structural failure. This report describes the FEAMAC/CARES methodology and discusses examples that illustrate the performance of the tool. A comprehensive example problem, simulating the progressive damage of laminated ceramic matrix composites under various off-axis loading conditions and including a double notched tensile specimen geometry, is described in a separate report.

Decoding flow unit evolution upon annealing from fracture morphology in metallic glasses

Energy Technology Data Exchange (ETDEWEB)

Gao, M., E-mail: gaomeng10@hotmail.com; Cao, X.F.; Ding, D.W.; Wang, B.B.; Wang, W.H., E-mail: whw@iphy.ac.cn

2017-02-16

The intrinsic correlation between the fracture morphology evolution and the structural heterogeneity of flow units in a typical Zr{sub 52.5}Ti{sub 5}Cu{sub 17.9}Ni{sub 14.6}Al{sub 10} (vit105) metallic glass (MG) upon annealing was investigated. By systematically tuning the annealing time at temperature below the glass transition temperature, a series of dimple-like fracture morphology were obtained, which is the unique fingerprint-like pattern for every annealing state. Based on the structural relaxation model of flow units, the evolution of the typical dimple sizes, the largest and smallest dimple size, with annealing were well fitted. Then the evolution of flow unit density was estimated from the fracture morphology evolution, which displays the same evolution trend with that measured from thermal relaxation. A stochastic dynamic model considering the interaction of activated flow units was proposed to analyze the effect of the initial flow unit density and the flow unit interaction intensity on the dynamic evolution of dimple distribution. Our work may provide a novel scheme to investigate the structural fingerprint information on flow units from fracture morphology, and enlighten the microscopic structural origin of the ductile-to-brittle transition during structural relaxation in MGs.

Transport in Stochastic Media

International Nuclear Information System (INIS)

Haran, O.; Shvarts, D.; Thieberger, R.

1998-01-01

Classical transport of neutral particles in a binary, scattering, stochastic media is discussed. It is assumed that the cross-sections of the constituent materials and their volume fractions are known. The inner structure of the media is stochastic, but there exist a statistical knowledge about the lump sizes, shapes and arrangement. The transmission through the composite media depends on the specific heterogeneous realization of the media. The current research focuses on the averaged transmission through an ensemble of realizations, frm which an effective cross-section for the media can be derived. The problem of one dimensional transport in stochastic media has been studied extensively [1]. In the one dimensional description of the problem, particles are transported along a line populated with alternating material segments of random lengths. The current work discusses transport in two-dimensional stochastic media. The phenomenon that is unique to the multi-dimensional description of the problem is obstacle bypassing. Obstacle bypassing tends to reduce the opacity of the media, thereby reducing its effective cross-section. The importance of this phenomenon depends on the manner in which the obstacles are arranged in the media. Results of transport simulations in multi-dimensional stochastic media are presented. Effective cross-sections derived from the simulations are compared against those obtained for the one-dimensional problem, and against those obtained from effective multi-dimensional models, which are partially based on a Markovian assumption

Fast stochastic simulation of biochemical reaction systems by alternative formulations of the chemical Langevin equation

KAUST Repository

Mélykúti, Bence

2010-01-01

The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when simulating (bio)chemical kinetics. The CLE model is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. The contribution of this work is that we observe and explore that the CLE is not a single equation, but a parametric family of equations, all of which give the same finite-dimensional distribution of the variables. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m1 pairs of reversible reactions and m2 irreversible reactions there is another, simple formulation of the CLE with only m1 + m2 Wiener processes, whereas the standard approach uses 2 m1 + m2. We demonstrate that there are considerable computational savings when using this latter formulation. Such transformations of the CLE do not cause a loss of accuracy and are therefore distinct from model reduction techniques. We illustrate our findings by considering alternative formulations of the CLE for a human ether a-go-go related gene ion channel model and the Goldbeter-Koshland switch. © 2010 American Institute of Physics.

An improved hybrid topology optimization approach coupling simulated annealing and SIMP (SA-SIMP)

International Nuclear Information System (INIS)

Garcia-Lopez, N P; Sanchez-Silva, M; Medaglia, A L; Chateauneuf, A

2010-01-01

The Solid Isotropic Material with Penalization (SIMP) methodology has been used extensively due to its versatility and ease of implementation. However, one of its main drawbacks is that resulting topologies exhibit areas of intermediate densities which lack any physical meaning. This paper presents a hybrid methodology which couples simulated annealing and SIMP (SA-SIMP) in order to achieve solutions which are stiffer and predominantly black and white. Under a look-ahead strategy, the algorithm gradually fixes or removes those elements whose density resulting from SIMP is intermediate. Different strategies for selecting and fixing the fractional elements are examined using benchmark examples, which show that topologies resulting from SA-SIMP are more rigid than SIMP and predominantly black and white.

REPAIR SHOP JOB SCHEDULING WITH PARALLEL OPERATORS AND MULTIPLE CONSTRAINTS USING SIMULATED ANNEALING

Directory of Open Access Journals (Sweden)

N. Shivasankaran

2013-04-01

Full Text Available Scheduling problems are generally treated as NP andash; complete combinatorial optimization problems which is a multi-objective and multi constraint one. Repair shop Job sequencing and operator allocation is one such NP andash; complete problem. For such problems, an efficient technique is required that explores a wide range of solution space. This paper deals with Simulated Annealing Technique, a Meta - heuristic to solve the complex Car Sequencing and Operator Allocation problem in a car repair shop. The algorithm is tested with several constraint settings and the solution quality exceeds the results reported in the literature with high convergence speed and accuracy. This algorithm could be considered as quite effective while other heuristic routine fails.

Quasi-continuous stochastic simulation framework for flood modelling

Science.gov (United States)

Moustakis, Yiannis; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas

2017-04-01

Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event.In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS),while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall.This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

A market based active/reactive dispatch including transformer taps and reactor and capacitor banks using Simulated Annealing

International Nuclear Information System (INIS)

Gomes, Mario Helder; Saraiva, Joao Tome

2009-01-01

This paper describes an optimization model to be used by System Operators in order to validate the economic schedules obtained by Market Operators together with the injections from Bilateral Contracts. These studies will be performed off-line in the day before operation and the developed model is based on adjustment bids submitted by generators and loads and it is used by System Operators if that is necessary to enforce technical or security constraints. This model corresponds to an enhancement of an approach described in a previous paper and it now includes discrete components as transformer taps and reactor and capacitor banks. The resulting mixed integer formulation is solved using Simulated Annealing, a well known metaheuristic specially suited for combinatorial problems. Once the Simulated Annealing converges and the values of the discrete variables are fixed, the resulting non-linear continuous problem is solved using Sequential Linear Programming to get the final solution. The developed model corresponds to an AC version, it includes constraints related with the capability diagram of synchronous generators and variables allowing the computation of the active power required to balance active losses. Finally, the paper includes a Case Study based on the IEEE 118 bus system to illustrate the results that it is possible to obtain and their interest. (author)

Simulated Annealing-based Optimal Proportional-Integral-Derivative (PID) Controller Design: A Case Study on Nonlinear Quadcopter Dynamics

Science.gov (United States)

Nemirsky, Kristofer Kevin

In this thesis, the history and evolution of rotor aircraft with simulated annealing-based PID application were reviewed and quadcopter dynamics are presented. The dynamics of a quadcopter were then modeled, analyzed, and linearized. A cascaded loop architecture with PID controllers was used to stabilize the plant dynamics, which was improved upon through the application of simulated annealing (SA). A Simulink model was developed to test the controllers and verify the functionality of the proposed control system design. In addition, the data that the Simulink model provided were compared with flight data to present the validity of derived dynamics as a proper mathematical model representing the true dynamics of the quadcopter system. Then, the SA-based global optimization procedure was applied to obtain optimized PID parameters. It was observed that the tuned gains through the SA algorithm produced a better performing PID controller than the original manually tuned one. Next, we investigated the uncertain dynamics of the quadcopter setup. After adding uncertainty to the gyroscopic effects associated with pitch-and-roll rate dynamics, the controllers were shown to be robust against the added uncertainty. A discussion follows to summarize SA-based algorithm PID controller design and performance outcomes. Lastly, future work on SA application on multi-input-multi-output (MIMO) systems is briefly discussed.

FluTE, a publicly available stochastic influenza epidemic simulation model.

Directory of Open Access Journals (Sweden)

Dennis L Chao

2010-01-01

Full Text Available Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2 and 2009 pandemic A(H1N1 influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.

FluTE, a publicly available stochastic influenza epidemic simulation model.

Science.gov (United States)

Chao, Dennis L; Halloran, M Elizabeth; Obenchain, Valerie J; Longini, Ira M

2010-01-29

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.

Reconstruction of X-rays spectra of clinical linear accelerators using the generalized simulated annealing method; Reconstrucao de espectros de raios-X de aceleradores lineares clinicos usando o metodo de recozimento simulado generalizado

Energy Technology Data Exchange (ETDEWEB)

Manrique, John Peter O.; Costa, Alessandro M., E-mail: johnp067@usp.br, E-mail: amcosta@usp.br [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil)

2016-07-01

The spectral distribution of megavoltage X-rays used in radiotherapy departments is a fundamental quantity from which, in principle, all relevant information required for radiotherapy treatments can be determined. To calculate the dose delivered to the patient who make radiation therapy, are used treatment planning systems (TPS), which make use of convolution and superposition algorithms and which requires prior knowledge of the photon fluence spectrum to perform the calculation of three-dimensional doses and thus ensure better accuracy in the tumor control probabilities preserving the normal tissue complication probabilities low. In this work we have obtained the photon fluence spectrum of X-ray of the SIEMENS ONCOR linear accelerator of 6 MV, using an character-inverse method to the reconstruction of the spectra of photons from transmission curves measured for different thicknesses of aluminum; the method used for reconstruction of the spectra is a stochastic technique known as generalized simulated annealing (GSA), based on the work of quasi-equilibrium statistic of Tsallis. For the validation of the reconstructed spectra we calculated the curve of percentage depth dose (PDD) for energy of 6 MV, using Monte Carlo simulation with Penelope code, and from the PDD then calculate the beam quality index TPR{sub 20/10}. (author)

Importance Sampling for Stochastic Timed Automata

DEFF Research Database (Denmark)

Jegourel, Cyrille; Larsen, Kim Guldstrand; Legay, Axel

2016-01-01

We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise cha......We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state...

Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization

Directory of Open Access Journals (Sweden)

Qi Xu

2012-01-01

Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race.

Science.gov (United States)

Warnke, Tom; Reinhardt, Oliver; Klabunde, Anna; Willekens, Frans; Uhrmacher, Adelinde M

2017-10-01

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

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

Metaheuristic simulation optimisation for the stochastic multi-retailer supply chain

Science.gov (United States)

Omar, Marina; Mustaffa, Noorfa Haszlinna H.; Othman, Siti Norsyahida

2013-04-01

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. A simulation optimization approach has been widely used in research nowadays on finding the best solution for decision-making process in Supply Chain Management (SCM) that generally faced a complexity with large sources of uncertainty and various decision factors. Metahueristic method is the most popular simulation optimization approach. However, very few researches have applied this approach in optimizing the simulation model for supply chains. Thus, this paper interested in evaluating the performance of metahueristic method for stochastic supply chains in determining the best flexible inventory replenishment parameters that minimize the total operating cost. The simulation optimization model is proposed based on the Bees algorithm (BA) which has been widely applied in engineering application such as training neural networks for pattern recognition. BA is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. This model considers an outbound centralised distribution system consisting of one supplier and 3 identical retailers and is assumed to be independent and identically distributed with unlimited supply capacity at supplier.

Project Evaluation and Cash Flow Forecasting by Stochastic Simulation

Directory of Open Access Journals (Sweden)

Odd A. Asbjørnsen

1983-10-01

Full Text Available The net present value of a discounted cash flow is used to evaluate projects. It is shown that the LaPlace transform of the cash flow time function is particularly useful when the cash flow profiles may be approximately described by ordinary linear differential equations in time. However, real cash flows are stochastic variables due to the stochastic nature of the disturbances during production.

FEAMAC/CARES Stochastic-Strength-Based Damage Simulation Tool for Ceramic Matrix Composites

Science.gov (United States)

Nemeth, Noel; Bednarcyk, Brett; Pineda, Evan; Arnold, Steven; Mital, Subodh; Murthy, Pappu; Bhatt, Ramakrishna

2016-01-01

Reported here is a coupling of two NASA developed codes: CARES (Ceramics Analysis and Reliability Evaluation of Structures) with the MAC/GMC (Micromechanics Analysis Code/ Generalized Method of Cells) composite material analysis code. The resulting code is called FEAMAC/CARES and is constructed as an Abaqus finite element analysis UMAT (user defined material). Here we describe the FEAMAC/CARES code and an example problem (taken from the open literature) of a laminated CMC in off-axis loading is shown. FEAMAC/CARES performs stochastic-strength-based damage simulation response of a CMC under multiaxial loading using elastic stiffness reduction of the failed elements.

Extrapolation of zircon fission-track annealing models

International Nuclear Information System (INIS)

Palissari, R.; Guedes, S.; Curvo, E.A.C.; Moreira, P.A.F.P.; Tello, C.A.; Hadler, J.C.

2013-01-01

One of the purposes of this study is to give further constraints on the temperature range of the zircon partial annealing zone over a geological time scale using data from borehole zircon samples, which have experienced stable temperatures for ∼1 Ma. In this way, the extrapolation problem is explicitly addressed by fitting the zircon annealing models with geological timescale data. Several empirical model formulations have been proposed to perform these calibrations and have been compared in this work. The basic form proposed for annealing models is the Arrhenius-type model. There are other annealing models, that are based on the same general formulation. These empirical model equations have been preferred due to the great number of phenomena from track formation to chemical etching that are not well understood. However, there are two other models, which try to establish a direct correlation between their parameters and the related phenomena. To compare the response of the different annealing models, thermal indexes, such as closure temperature, total annealing temperature and the partial annealing zone, have been calculated and compared with field evidence. After comparing the different models, it was concluded that the fanning curvilinear models yield the best agreement between predicted index temperatures and field evidence. - Highlights: ► Geological data were used along with lab data for improving model extrapolation. ► Index temperatures were simulated for testing model extrapolation. ► Curvilinear Arrhenius models produced better geological temperature predictions

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

Science.gov (United States)

Rowat, Peter F; Greenwood, Priscilla E

2014-01-01

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Propagating self-sustained annealing of radiation-induced interstitial complexes

International Nuclear Information System (INIS)

Bokov, P M; Selyshchev, P A

2016-01-01

A propagating self-sustained annealing of radiation induced defects as a result of thermal-concentration instability is studied. The defects that are considered in the model are complexes. Each of them consists of one atom of impunity and of one interstitial atom. Crystal with defects has extra energy which is transformed into heat during defect annealing. Simulation of the auto-wave of annealing has been performed. The front and the speed of the auto-wave have been obtained. It is shown that annealing occurs in a narrow region of time and space. There are two kinds of such annealing behaviour. In the first case the speed of the auto-wave oscillates near its constant mean value and the front of temperature oscillates in a complex way. In the second case the speed of propagation is constant and fronts of temperature and concentration look like sigmoid functions. (paper)

Stochastic Rotation Dynamics simulations of wetting multi-phase flows

Science.gov (United States)

Hiller, Thomas; Sanchez de La Lama, Marta; Brinkmann, Martin

2016-06-01

Multi-color Stochastic Rotation Dynamics (SRDmc) has been introduced by Inoue et al. [1,2] as a particle based simulation method to study the flow of emulsion droplets in non-wetting microchannels. In this work, we extend the multi-color method to also account for different wetting conditions. This is achieved by assigning the color information not only to fluid particles but also to virtual wall particles that are required to enforce proper no-slip boundary conditions. To extend the scope of the original SRDmc algorithm to e.g. immiscible two-phase flow with viscosity contrast we implement an angular momentum conserving scheme (SRD+mc). We perform extensive benchmark simulations to show that a mono-phase SRDmc fluid exhibits bulk properties identical to a standard SRD fluid and that SRDmc fluids are applicable to a wide range of immiscible two-phase flows. To quantify the adhesion of a SRD+mc fluid in contact to the walls we measure the apparent contact angle from sessile droplets in mechanical equilibrium. For a further verification of our wettability implementation we compare the dewetting of a liquid film from a wetting stripe to experimental and numerical studies of interfacial morphologies on chemically structured surfaces.

Compatible topologies and parameters for NMR structure determination of carbohydrates by simulated annealing.

Science.gov (United States)

Feng, Yingang

2017-01-01

The use of NMR methods to determine the three-dimensional structures of carbohydrates and glycoproteins is still challenging, in part because of the lack of standard protocols. In order to increase the convenience of structure determination, the topology and parameter files for carbohydrates in the program Crystallography & NMR System (CNS) were investigated and new files were developed to be compatible with the standard simulated annealing protocols for proteins and nucleic acids. Recalculating the published structures of protein-carbohydrate complexes and glycosylated proteins demonstrates that the results are comparable to the published structures which employed more complex procedures for structure calculation. Integrating the new carbohydrate parameters into the standard structure calculation protocol will facilitate three-dimensional structural study of carbohydrates and glycosylated proteins by NMR spectroscopy.

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

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.

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...

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

A stochastic six-degree-of-freedom flight simulator for passively controlled high power rockets

OpenAIRE

Box, Simon; Bishop, Christopher M.; Hunt, Hugh

2011-01-01

This paper presents a method for simulating the flight of a passively controlled rocket in six degrees of freedom, and the descent under parachute in three degrees of freedom, Also presented is a method for modelling the uncertainty in both the rocket dynamics and the atmospheric conditions using stochastic parameters and the Monte-Carlo method. Included within this we present a method for quantifying the uncertainty in the atmospheric conditions using historical atmospheric data. The core si...

American option pricing with stochastic volatility processes

Directory of Open Access Journals (Sweden)

Ping LI

2017-12-01

Full Text Available In order to solve the problem of option pricing more perfectly, the option pricing problem with Heston stochastic volatility model is considered. The optimal implementation boundary of American option and the conditions for its early execution are analyzed and discussed. In view of the fact that there is no analytical American option pricing formula, through the space discretization parameters, the stochastic partial differential equation satisfied by American options with Heston stochastic volatility is transformed into the corresponding differential equations, and then using high order compact finite difference method, numerical solutions are obtained for the option price. The numerical experiments are carried out to verify the theoretical results and simulation. The two kinds of optimal exercise boundaries under the conditions of the constant volatility and the stochastic volatility are compared, and the results show that the optimal exercise boundary also has stochastic volatility. Under the setting of parameters, the behavior and the nature of volatility are analyzed, the volatility curve is simulated, the calculation results of high order compact difference method are compared, and the numerical option solution is obtained, so that the method is verified. The research result provides reference for solving the problems of option pricing under stochastic volatility such as multiple underlying asset option pricing and barrier option pricing.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

Science.gov (United States)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

Science.gov (United States)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

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...

STOCHASTIC SIMULATION FOR BUFFELGRASS (Cenchrus ciliaris L. PASTURES IN MARIN, N. L., MEXICO

Directory of Open Access Journals (Sweden)

José Romualdo Martínez-López

2014-04-01

Full Text Available A stochastic simulation model was constructed to determine the response of net primary production of buffelgrass (Cenchrus ciliaris L. and its dry matter intake by cattle, in Marín, NL, México. Buffelgrass is very important for extensive livestock industry in arid and semiarid areas of northeastern Mexico. To evaluate the behavior of the model by comparing the model results with those reported in the literature was the objective in this experiment. Model simulates the monthly production of dry matter of green grass, as well as its conversion to senescence and dry grass and eventually to mulch, depending on precipitation and temperature. Model also simulates consumption of green and dry grass for cattle. The stocking rate used in the model simulation was 2 hectares per animal unit. Annual production ranged from 4.5 to 10.2 t of dry matter per hectare with annual rainfall of 300 to 704 mm, respectively. Total annual intake required per animal unit was estimated at 3.6 ton. Simulated net primary production coincides with reports in the literature, so the model was evaluated successfully.

Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes

Science.gov (United States)

Elliott, Thomas J.; Gu, Mile

2018-03-01

Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.

Composition dependent thermal annealing behaviour of ion tracks in apatite

Energy Technology Data Exchange (ETDEWEB)

Nadzri, A., E-mail: allina.nadzri@anu.edu.au [Department of Electronic Materials Engineering, Research School of Physics and Engineering, Australian National University, Canberra, ACT 2601 (Australia); Schauries, D.; Mota-Santiago, P.; Muradoglu, S. [Department of Electronic Materials Engineering, Research School of Physics and Engineering, Australian National University, Canberra, ACT 2601 (Australia); Trautmann, C. [GSI Helmholtz Centre for Heavy Ion Research, Planckstrasse 1, 64291 Darmstadt (Germany); Technische Universität Darmstadt, 64287 Darmstadt (Germany); Gleadow, A.J.W. [School of Earth Science, University of Melbourne, Melbourne, VIC 3010 (Australia); Hawley, A. [Australian Synchrotron, 800 Blackburn Road, Clayton, VIC 3168 (Australia); Kluth, P. [Department of Electronic Materials Engineering, Research School of Physics and Engineering, Australian National University, Canberra, ACT 2601 (Australia)

2016-07-15

Natural apatite samples with different F/Cl content from a variety of geological locations (Durango, Mexico; Mud Tank, Australia; and Snarum, Norway) were irradiated with swift heavy ions to simulate fission tracks. The annealing kinetics of the resulting ion tracks was investigated using synchrotron-based small-angle X-ray scattering (SAXS) combined with ex situ annealing. The activation energies for track recrystallization were extracted and consistent with previous studies using track-etching, tracks in the chlorine-rich Snarum apatite are more resistant to annealing than in the other compositions.

Stochastic Systems Uncertainty Quantification and Propagation

CERN Document Server

Grigoriu, Mircea

2012-01-01

Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: ·         A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis   ·          Probabilistic models for random variables an...

The Separatrix Algorithm for synthesis and analysis of stochastic simulations with applications in disease modeling.

Directory of Open Access Journals (Sweden)

Daniel J Klein

Full Text Available Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties. To facilitate the decision making process in the context of a goal-oriented objective (e.g., eradicate polio by [Formula: see text], stochastic models can be used to map the probability of achieving the goal as a function of parameters. Each run of a stochastic model can be viewed as a Bernoulli trial in which "success" is returned if and only if the goal is achieved in simulation. However, each run can take a significant amount of time to complete, and many replicates are required to characterize each point in parameter space, so specialized algorithms are required to locate desirable interventions. To address this need, we present the Separatrix Algorithm, which strategically locates parameter combinations that are expected to achieve the goal with a user-specified probability of success (e.g. 95%. Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice. The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria.

Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making

International Nuclear Information System (INIS)

Tahvili, Sahar; Österberg, Jonas; Silvestrov, Sergei; Biteus, Jonas

2014-01-01

One of the most important factors in the operations of many cooperations today is to maximize profit and one important tool to that effect is the optimization of maintenance activities. Maintenance activities is at the largest level divided into two major areas, corrective maintenance (CM) and preventive maintenance (PM). When optimizing maintenance activities, by a maintenance plan or policy, we seek to find the best activities to perform at each point in time, be it PM or CM. We explore the use of stochastic simulation, genetic algorithms and other tools for solving complex maintenance planning optimization problems in terms of a suggested framework model based on discrete event simulation

Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making

Energy Technology Data Exchange (ETDEWEB)

Tahvili, Sahar [Mälardalen University (Sweden); Österberg, Jonas; Silvestrov, Sergei [Division of Applied Mathematics, Mälardalen University (Sweden); Biteus, Jonas [Scania CV (Sweden)

2014-12-10

One of the most important factors in the operations of many cooperations today is to maximize profit and one important tool to that effect is the optimization of maintenance activities. Maintenance activities is at the largest level divided into two major areas, corrective maintenance (CM) and preventive maintenance (PM). When optimizing maintenance activities, by a maintenance plan or policy, we seek to find the best activities to perform at each point in time, be it PM or CM. We explore the use of stochastic simulation, genetic algorithms and other tools for solving complex maintenance planning optimization problems in terms of a suggested framework model based on discrete event simulation.

Introduction to stochastic analysis integrals and differential equations

CERN Document Server

Mackevicius, Vigirdas

2013-01-01

This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances. The presentation is based on the naïve stochastic integration, rather than on abstract theories of measure and stochastic processes. The proofs are rather simple for practitioners and, at the same time, rather rigorous for mathematicians. Detailed application examples in natural sciences and finance are presented. Much attention is paid to simulation diffusion pro

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.

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

Science.gov (United States)

Williams Colin P.

1999-01-01

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

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

of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth. - Highlights: • A hybrid method for stochastic multi-scale models of cells populations that extends existing hybrid methods for reaction–diffusion system. • Our analysis unveils non-trivial macroscopic effects triggered by noise at the level of structuring variables. • Our hybrid method hugely speeds up age-structured SSA simulations while preserving stochastic effects.

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure Induced by HZE Particles

Science.gov (United States)

Ponomarev, Artem; Plante, Ianik; George, Kerry; Wu, Honglu

2014-01-01

The formation of double-strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte-Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high-energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation-induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05-0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta-rays becomes more important.

Extended Information Ratio for Portfolio Optimization Using Simulated Annealing with Constrained Neighborhood

Science.gov (United States)

Orito, Yukiko; Yamamoto, Hisashi; Tsujimura, Yasuhiro; Kambayashi, Yasushi

The portfolio optimizations are to determine the proportion-weighted combination in the portfolio in order to achieve investment targets. This optimization is one of the multi-dimensional combinatorial optimizations and it is difficult for the portfolio constructed in the past period to keep its performance in the future period. In order to keep the good performances of portfolios, we propose the extended information ratio as an objective function, using the information ratio, beta, prime beta, or correlation coefficient in this paper. We apply the simulated annealing (SA) to optimize the portfolio employing the proposed ratio. For the SA, we make the neighbor by the operation that changes the structure of the weights in the portfolio. In the numerical experiments, we show that our portfolios keep the good performances when the market trend of the future period becomes different from that of the past period.

A Simulated Annealing method to solve a generalized maximal covering location problem

Directory of Open Access Journals (Sweden)

M. Saeed Jabalameli

2011-04-01

Full Text Available The maximal covering location problem (MCLP seeks to locate a predefined number of facilities in order to maximize the number of covered demand points. In a classical sense, MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations are extended: the gradual cover models, the cooperative cover models, and the variable radius models. In this paper, we develop a special form of MCLP which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed problem has many applications such as locating cell phone towers. The model is formulated as a mixed integer non-linear programming (MINLP. In addition, a simulated annealing algorithm is used to solve the resulted problem and the performance of the proposed method is evaluated with a set of randomly generated problems.

Stochastic self-propagating star formation in three-dimensional disk galaxy simulations

International Nuclear Information System (INIS)

Statler, T.; Comins, N.; Smith, B.F.

1983-01-01

Stochastic self-propagating star formation (SSPSF) is a process of forming new stars through the compression of the interstellar medium by supernova shock waves. Coupling this activity with galactic differential rotation produces spiral structure in two-dimensional disk galaxy simulations. In this paper the first results of a three-dimensional SSPSF simulation of disk galaxies are reported. Our model generates less impressive spirals than do the two-dimensional simulations. Although some spirals do appear in equilibrium, more frequently we observe spirals as non-equilibrium states of the models: as the spiral arms evolve, they widen until the spiral structure is no longer discernible. The two free parameters that we vary in this study are the probability of star formation due to a recent, nearby explosion, and the relaxation time for the interstellar medium to return to a condition of maximum star formation after it has been cleared out by an explosion and subsequent star formation. We find that equilibrium spiral structure is formed over a much smaller range of these parameters in our three-dimensional SSPSF models than in similar two-dimensional models. We discuss possible reasons for these results as well as improvements on the model which are being explored

A conditional stochastic weather generator for seasonal to multi-decadal simulations

Science.gov (United States)

Verdin, Andrew; Rajagopalan, Balaji; Kleiber, William; Podestá, Guillermo; Bert, Federico

2018-01-01

We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

A simulation-based interval two-stage stochastic model for agricultural nonpoint source pollution control through land retirement

International Nuclear Information System (INIS)

Luo, B.; Li, J.B.; Huang, G.H.; Li, H.L.

2006-01-01

This study presents a simulation-based interval two-stage stochastic programming (SITSP) model for agricultural nonpoint source (NPS) pollution control through land retirement under uncertain conditions. The modeling framework was established by the development of an interval two-stage stochastic program, with its random parameters being provided by the statistical analysis of the simulation outcomes of a distributed water quality approach. The developed model can deal with the tradeoff between agricultural revenue and 'off-site' water quality concern under random effluent discharge for a land retirement scheme through minimizing the expected value of long-term total economic and environmental cost. In addition, the uncertainties presented as interval numbers in the agriculture-water system can be effectively quantified with the interval programming. By subdividing the whole agricultural watershed into different zones, the most pollution-related sensitive cropland can be identified and an optimal land retirement scheme can be obtained through the modeling approach. The developed method was applied to the Swift Current Creek watershed in Canada for soil erosion control through land retirement. The Hydrological Simulation Program-FORTRAN (HSPF) was used to simulate the sediment information for this case study. Obtained results indicate that the total economic and environmental cost of the entire agriculture-water system can be limited within an interval value for the optimal land retirement schemes. Meanwhile, a best and worst land retirement scheme was obtained for the study watershed under various uncertainties

Displacement cascades and defect annealing in tungsten, Part III: The sensitivity of cascade annealing in tungsten to the values of kinetic parameters

Energy Technology Data Exchange (ETDEWEB)

Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.; Roche, Kenneth J.; Kurtz, Richard J.; Wirth, Brian D.

2015-07-01

Object kinetic Monte Carlo (OKMC) simulations have been performed to investigate various aspects of cascade aging in bulk tungsten and to determine the sensitivity of the results to the kinetic parameters. The primary focus is on how the kinetic parameters affect the initial recombination of defects in the first few ns of a simulation. The simulations were carried out using the object kinetic Monte Carlo (OKMC) code KSOME (kinetic simulations of microstructure evolution), using a database of cascades obtained from results of molecular dynamics (MD) simulations at various primary knock-on atom (PKA) energies and directions at temperatures of 300, 1025 and 2050 K. The OKMC model was parameterized using defect migration barriers and binding energies from ab initio calculations. Results indicate that, due to the disparate mobilities of SIA and vacancy clusters in tungsten, annealing is dominated by SIA migration even at temperatures as high as 2050 K. For 100 keV cascades initiated at 300 K recombination is dominated by annihilation of large defect clusters. But for all other PKA energies and temperatures most of the recombination is due to the migration and rotation of small SIA clusters, while all the large SIA clusters escape the cubic simulation cell. The inverse U-shape behavior exhibited by the annealing efficiency as a function of temperature curve, especially for cascades of large PKA energies, is due to asymmetry in SIA and vacancy clustering assisted by the large difference in mobilities of SIAs and vacancies. This annealing behavior is unaffected by the dimensionality of SIA migration persists over a broad range of relative mobilities of SIAs and vacancies.

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

Science.gov (United States)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

Stochastic reaction-diffusion algorithms for macromolecular crowding

Science.gov (United States)

Sturrock, Marc

2016-06-01

Compartment-based (lattice-based) reaction-diffusion algorithms are often used for studying complex stochastic spatio-temporal processes inside cells. In this paper the influence of macromolecular crowding on stochastic reaction-diffusion simulations is investigated. Reaction-diffusion processes are considered on two different kinds of compartmental lattice, a cubic lattice and a hexagonal close packed lattice, and solved using two different algorithms, the stochastic simulation algorithm and the spatiocyte algorithm (Arjunan and Tomita 2010 Syst. Synth. Biol. 4, 35-53). Obstacles (modelling macromolecular crowding) are shown to have substantial effects on the mean squared displacement and average number of molecules in the domain but the nature of these effects is dependent on the choice of lattice, with the cubic lattice being more susceptible to the effects of the obstacles. Finally, improvements for both algorithms are presented.

Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization

Science.gov (United States)

Soltani-Mohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi

2016-10-01

One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.

Geometric integrators for stochastic rigid body dynamics

KAUST Repository

Tretyakov, Mikhail

2016-01-05

Geometric integrators play an important role in simulating dynamical systems on long time intervals with high accuracy. We will illustrate geometric integration ideas within the stochastic context, mostly on examples of stochastic thermostats for rigid body dynamics. The talk will be mainly based on joint recent work with Rusland Davidchak and Tom Ouldridge.

Geometric integrators for stochastic rigid body dynamics

KAUST Repository

Tretyakov, Mikhail

2016-01-01

Geometric integrators play an important role in simulating dynamical systems on long time intervals with high accuracy. We will illustrate geometric integration ideas within the stochastic context, mostly on examples of stochastic thermostats for rigid body dynamics. The talk will be mainly based on joint recent work with Rusland Davidchak and Tom Ouldridge.

Verification of HYDRASTAR - A code for stochastic continuum simulation of groundwater flow

International Nuclear Information System (INIS)

Norman, S.

1991-07-01

HYDRASTAR is a code developed at Starprog AB for use in the SKB 91 performance assessment project with the following principal function: - Reads the actual conductivity measurements from a file created from the data base GEOTAB. - Regularizes the measurements to a user chosen calculation scale. - Generates three dimensional unconditional realizations of the conductivity field by using a supplied model of the conductivity field as a stochastic function. - Conditions the simulated conductivity field on the actual regularized measurements. - Reads the boundary conditions from a regional deterministic NAMMU computation. - Calculates the hydraulic head field, Darcy velocity field, stream lines and water travel times by solving the stationary hydrology equation and the streamline equation obtained with the velocities calculated from Darcy's law. - Generates visualizations of the realizations if desired. - Calculates statistics such as semivariograms and expectation values of the output fields by repeating the above procedure by iterations of the Monte Carlo type. When using computer codes for safety assessment purpose validation and verification of the codes are important. Thus this report describes a work performed with the goal of verifying parts of HYDRASTAR. The verification described in this report uses comparisons with two other solutions of related examples: A. Comparison with a so called perturbation solution of the stochastical stationary hydrology equation. This as an analytical approximation of the stochastical stationary hydrology equation valid in the case of small variability of the unconditional random conductivity field. B. Comparison with the (Hydrocoin, 1988), case 2. This is a classical example of a hydrology problem with a deterministic conductivity field. The principal feature of the problem is the presence of narrow fracture zones with high conductivity. the compared output are the hydraulic head field and a number of stream lines originating from a

XMDS2: Fast, scalable simulation of coupled stochastic partial differential equations

Science.gov (United States)

Dennis, Graham R.; Hope, Joseph J.; Johnsson, Mattias T.

2013-01-01

XMDS2 is a cross-platform, GPL-licensed, open source package for numerically integrating initial value problems that range from a single ordinary differential equation up to systems of coupled stochastic partial differential equations. The equations are described in a high-level XML-based script, and the package generates low-level optionally parallelised C++ code for the efficient solution of those equations. It combines the advantages of high-level simulations, namely fast and low-error development, with the speed, portability and scalability of hand-written code. XMDS2 is a complete redesign of the XMDS package, and features support for a much wider problem space while also producing faster code. Program summaryProgram title: XMDS2 Catalogue identifier: AENK_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENK_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 2 No. of lines in distributed program, including test data, etc.: 872490 No. of bytes in distributed program, including test data, etc.: 45522370 Distribution format: tar.gz Programming language: Python and C++. Computer: Any computer with a Unix-like system, a C++ compiler and Python. Operating system: Any Unix-like system; developed under Mac OS X and GNU/Linux. RAM: Problem dependent (roughly 50 bytes per grid point) Classification: 4.3, 6.5. External routines: The external libraries required are problem-dependent. Uses FFTW3 Fourier transforms (used only for FFT-based spectral methods), dSFMT random number generation (used only for stochastic problems), MPI message-passing interface (used only for distributed problems), HDF5, GNU Scientific Library (used only for Bessel-based spectral methods) and a BLAS implementation (used only for non-FFT-based spectral methods). Nature of problem: General coupled initial-value stochastic partial differential equations. Solution method: Spectral method

Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

International Nuclear Information System (INIS)

Mohanta, Dusmanta Kumar; Sadhu, Pradip Kumar; Chakrabarti, R.

2007-01-01

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power

Characterisation of amorphous silicon alloys by RBS/ERD with self consistent data analysis using simulated annealing

International Nuclear Information System (INIS)

Barradas, N.P.; Wendler, E.; Jeynes, C.; Summers, S.; Reehal, H.S.; Summers, S.

1999-01-01

Full text: Hydrogenated amorphous silicon films are deposited by CVD onto insulating (silica) substrates for the fabrication of solar cells. 1.5MeV 4 He ERD/RBS is applied to the films, and a self consistent depth profile of Si and H using the simulated annealing (SA) algorithm was obtained for each sample. The analytical procedure is described in detail, and the confidence limits of the profiles are obtained using the Markov Chain Monte Carlo method which is a natural extension of the SA algorithm. We show how the results are of great benefit to the growers

Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data.

Science.gov (United States)

Zhu, Lin; Gong, Huili; Chen, Yun; Li, Xiaojuan; Chang, Xiang; Cui, Yijiao

2016-03-01

Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie's law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling.

STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION

Directory of Open Access Journals (Sweden)

Nataša Krejić

2014-12-01

Full Text Available This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization. The focus of this presentation is on the gradient based Stochastic Approximation and Sample Average Approximation methods. The concept of stochastic gradient approximation of the true gradient can be successfully extended to deterministic problems. Methods of this kind are presented for the data fitting and machine learning problems.

Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

Science.gov (United States)

Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

Multi–criteria evaluation and simulated annealing for delimiting high priority habitats of Alectoris chukar and Phasianus colchicus in Iran

Directory of Open Access Journals (Sweden)

Momeni Dehaghi, I.

2018-01-01

Full Text Available Habitat degradation and hunting are among the most important causes of population decline for Alectoris chukar and Phasianus colchicus, two of the most threatened game species in the Golestan Province of Iran. Limited data on distribution and location of high–quality habitats for the two species make conservation efforts more difficult in the province. We used multi–criteria evaluation (MCE as a coarse–filter approach to refine the general distribution areas into habitat suitability maps for the species. We then used these maps as input to simulated annealing as a heuristic algorithm through Marxan in order to prioritize areas for conservation of the two species. To find the optimal solution, we tested various boundary length modifier (BLM values in the simulated annealing process. Our results showed that the MCE approach was useful to refine general habitat maps. Assessment of the selected reserves confirmed the suitability of the selected areas (mainly neighboring the current reserves making their management easier and more feasible. The total area of the selected reserves was about 476 km2. As current reserves of the Golestan Province represent only 23 % of the optimal area, further protected areas should be considered to efficiently conserve these two species.

Non-stoquastic Hamiltonians in quantum annealing via geometric phases

Science.gov (United States)

Vinci, Walter; Lidar, Daniel A.

2017-09-01

We argue that a complete description of quantum annealing implemented with continuous variables must take into account the non-adiabatic Aharonov-Anandan geometric phase that arises when the system Hamiltonian changes during the anneal. We show that this geometric effect leads to the appearance of non-stoquasticity in the effective quantum Ising Hamiltonians that are typically used to describe quantum annealing with flux qubits. We explicitly demonstrate the effect of this geometric non-stoquasticity when quantum annealing is performed with a system of one and two coupled flux qubits. The realization of non-stoquastic Hamiltonians has important implications from a computational complexity perspective, since it is believed that in many cases quantum annealing with stoquastic Hamiltonians can be efficiently simulated via classical algorithms such as Quantum Monte Carlo. It is well known that the direct implementation of non-stoquastic Hamiltonians with flux qubits is particularly challenging. Our results suggest an alternative path for the implementation of non-stoquasticity via geometric phases that can be exploited for computational purposes.

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.

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure iIduced by HZE Particles

Science.gov (United States)

Ponomarev, Artem; Plante, Ianik; George, Kerry; Wu, Honglu

2014-01-01

The formation of double-strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte-Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high-energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation-induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05-0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta-rays becomes more important.

Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Supriya Dhabal

2014-01-01

Full Text Available We present a novel hybrid algorithm based on particle swarm optimization (PSO and simulated annealing (SA for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

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.

Real option valuation of power transmission investments by stochastic simulation

International Nuclear Information System (INIS)

Pringles, Rolando; Olsina, Fernando; Garcés, Francisco

2015-01-01

Network expansions in power markets usually lead to investment decisions subject to substantial irreversibility and uncertainty. Hence, investors need valuing the flexibility to change decisions as uncertainty unfolds progressively. Real option analysis is an advanced valuation technique that enables planners to take advantage of market opportunities while preventing or mitigating losses if future conditions evolve unfavorably. In the past, many approaches for valuing real options have been developed. However, applying these methods to value transmission projects is often inappropriate as revenue cash flows are path-dependent and affected by a myriad of uncertain variables. In this work, a valuation technique based on stochastic simulation and recursive dynamic programming, called Least-Square Monte Carlo, is applied to properly value the deferral option in a transmission investment. The effect of option's maturity, the initial outlay and the capital cost upon the value of the postponement option is investigated. Finally, sensitivity analysis determines optimal decision regions to execute, postpone or reject the investment projects. - Highlights: • A modern investment appraisal method is applied to value power transmission projects. • The value of the option to postpone decision to invest in transmission projects is assessed. • Simulation methods are best suited for valuing real options in transmission investments

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.

Dynamics of non-holonomic systems with stochastic transport

Science.gov (United States)

Holm, D. D.; Putkaradze, V.

2018-01-01

This paper formulates a variational approach for treating observational uncertainty and/or computational model errors as stochastic transport in dynamical systems governed by action principles under non-holonomic constraints. For this purpose, we derive, analyse and numerically study the example of an unbalanced spherical ball rolling under gravity along a stochastic path. Our approach uses the Hamilton-Pontryagin variational principle, constrained by a stochastic rolling condition, which we show is equivalent to the corresponding stochastic Lagrange-d'Alembert principle. In the example of the rolling ball, the stochasticity represents uncertainty in the observation and/or error in the computational simulation of the angular velocity of rolling. The influence of the stochasticity on the deterministically conserved quantities is investigated both analytically and numerically. Our approach applies to a wide variety of stochastic, non-holonomically constrained systems, because it preserves the mathematical properties inherited from the variational principle.

Numerical and experimental simulation of mechanical and microstructural transformations in Batch annealing steels

International Nuclear Information System (INIS)

Monsalve, A.; Artigas, A.; Celentano, D.; Melendez, F.

2004-01-01

The heating and cooling curves during batch annealing process of low carbon steel have been modeled using the finite element technique. This has allowed to predict the transient thermal profile for every point of the annealed coils, particularly for the hottest and coldest ones. Through experimental measurements, the results have been adequately validated since a good agreement has been found between experimental values and those predicted by the model. Moreover, an Avrami recrystallization model. Moreover, and Avrami recrystallization model has been coupled to this thermal balance computation. Interrupted annealing experiments have been made by measuring the recrystallized fraction on the extreme points of the coil foe different times. These data gave the possibility to validate the developed recrystallization model through a reasonably good numerical-experimental fittings. (Author) 6 refs

Stochastic development regression using method of moments

DEFF Research Database (Denmark)

Kühnel, Line; Sommer, Stefan Horst

2017-01-01

This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....

Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

Science.gov (United States)

Allah Taleizadeh, Ata; Aryanezhad, Mir-Bahador; Niaki, Seyed Taghi Akhavan

Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.

Handbook of simulation optimization

CERN Document Server

Fu, Michael C

2014-01-01

The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science,...

Trajectory averaging for stochastic approximation MCMC algorithms

KAUST Repository

Liang, Faming

2010-10-01

The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.

LIBRJMCMC: AN OPEN-SOURCE GENERIC C++ LIBRARY FOR STOCHASTIC OPTIMIZATION

Directory of Open Access Journals (Sweden)

M. Brédif

2012-07-01

Full Text Available The librjmcmc is an open source C++ library that solves optimization problems using a stochastic framework. The library is primarily intended for but not limited to research purposes in computer vision, photogrammetry and remote sensing, as it has initially been developed in the context of extracting building footprints from digital elevation models using a marked point process of rectangles. It has been designed to be both highly modular and extensible, and have computational times comparable to a code specifically designed for a particular application, thanks to the powerful paradigms of metaprogramming and generic programming. The proposed stochastic optimization is built on the coupling of a stochastic Reversible-Jump Markov Chain Monte Carlo (RJMCMC sampler and a simulated annealing relaxation. This framework allows, with theoretical guarantees, the optimization of an unrestricted objective function without requiring any initial solution. The modularity of our library allows the processing of any kind of input data, whether they are 1D signals (e.g. LiDAR or SAR waveforms, 2D images, 3D point clouds... The library user has just to define a few modules describing its domain specific context: the encoding of a configuration (e.g. its object type in a marked point process context, reversible jump kernels (e.g. birth, death, modifications..., the optimized energies (e.g. data and regularization terms and the probabilized search space given by the reference process. Similar to this extensibility in the application domain, concepts are clearly and orthogonally separated such that it is straightforward to customize the convergence test, the temperature schedule, or to add visitors enabling visual feedback during the optimization. The library offers dedicated modules for marked point processes, allowing the user to optimize a Maximum A Posteriori (MAP criterion with an image data term energy on a marked point process of rectangles.

Rapid thermal pulse annealing

International Nuclear Information System (INIS)

Miller, M.G.; Koehn, B.W.; Chaplin, R.L.

1976-01-01

Characteristics of recovery processes have been investigated for cases of heating a sample to successively higher temperatures by means of isochronal annealing or by using a rapid pulse annealing. A recovery spectra shows the same features independent of which annealing procedure is used. In order to determine which technique provides the best resolution, a study was made of how two independent first-order processes are separated for different heating rates and time increments of the annealing pulses. It is shown that the pulse anneal method offers definite advantages over isochronal annealing when annealing for short time increments. Experimental data by means of the pulse anneal techniques are given for the various substages of stage I of aluminium. (author)

Stochastic structure of annual discharges of large European rivers

Directory of Open Access Journals (Sweden)

Stojković Milan

2015-03-01

Full Text Available Water resource has become a guarantee for sustainable development on both local and global scales. Exploiting water resources involves development of hydrological models for water management planning. In this paper we present a new stochastic model for generation of mean annul flows. The model is based on historical characteristics of time series of annual flows and consists of the trend component, long-term periodic component and stochastic component. The rest of specified components are model errors which are represented as a random time series. The random time series is generated by the single bootstrap model (SBM. Stochastic ensemble of error terms at the single hydrological station is formed using the SBM method. The ultimate stochastic model gives solutions of annual flows and presents a useful tool for integrated river basin planning and water management studies. The model is applied for ten large European rivers with long observed period. Validation of model results suggests that the stochastic flows simulated by the model can be used for hydrological simulations in river basins.

Open-System Quantum Annealing in Mean-Field Models with Exponential Degeneracy*

Directory of Open Access Journals (Sweden)

Kostyantyn Kechedzhi

2016-05-01

Full Text Available Real-life quantum computers are inevitably affected by intrinsic noise resulting in dissipative nonunitary dynamics realized by these devices. We consider an open-system quantum annealing algorithm optimized for such a realistic analog quantum device which takes advantage of noise-induced thermalization and relies on incoherent quantum tunneling at finite temperature. We theoretically analyze the performance of this algorithm considering a p-spin model that allows for a mean-field quasiclassical solution and, at the same time, demonstrates the first-order phase transition and exponential degeneracy of states, typical characteristics of spin glasses. We demonstrate that finite-temperature effects introduced by the noise are particularly important for the dynamics in the presence of the exponential degeneracy of metastable states. We determine the optimal regime of the open-system quantum annealing algorithm for this model and find that it can outperform simulated annealing in a range of parameters. Large-scale multiqubit quantum tunneling is instrumental for the quantum speedup in this model, which is possible because of the unusual nonmonotonous temperature dependence of the quantum-tunneling action in this model, where the most efficient transition rate corresponds to zero temperature. This model calculation is the first analytically tractable example where open-system quantum annealing algorithm outperforms simulated annealing, which can, in principle, be realized using an analog quantum computer.

Convection methodology for fission track annealing: direct and inverse numerical simulations in the multi-exponential case

International Nuclear Information System (INIS)

Miellou, J.C.; Igli, H.; Grivet, M.; Rebetez, M.; Chambaudet, A.

1994-01-01

In minerals, the uranium fission tracks are sensitive to temperature and time. The consequence is that the etchable lengths are reduced. To simulate the phenomenon, at the last International Conference on Nuclear Tracks in solids at Beijing in 1992, we proposed a convection model for fission track annealing based on a reaction situation associated with only one activation energy. Moreover a simple inverse method based on the resolution of an ordinary differential equation was described, making it possible to retrace the thermal history in this mono-exponential situation. The aim of this paper is to consider a more involved class of models including multi-exponentials associated with several activation energies. We shall describe in this framework the modelling of the direct phenomenon and the resolution of the inverse problem. Results of numerical simulations and comparison with the mono-exponential case will be presented. 5 refs. (author)

Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing.

Science.gov (United States)

Barca, E; Castrignanò, A; Buttafuoco, G; De Benedetto, D; Passarella, G

2015-07-01

Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The

Stochastic synchronization of coupled neural networks with intermittent control

International Nuclear Information System (INIS)

Yang Xinsong; Cao Jinde

2009-01-01

In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.

Simulated Annealing Technique for Routing in a Rectangular Mesh Network

Directory of Open Access Journals (Sweden)

Noraziah Adzhar

2014-01-01

Full Text Available In the process of automatic design for printed circuit boards (PCBs, the phase following cell placement is routing. On the other hand, routing process is a notoriously difficult problem, and even the simplest routing problem which consists of a set of two-pin nets is known to be NP-complete. In this research, our routing region is first tessellated into a uniform Nx×Ny array of square cells. The ultimate goal for a routing problem is to achieve complete automatic routing with minimal need for any manual intervention. Therefore, shortest path for all connections needs to be established. While classical Dijkstra’s algorithm guarantees to find shortest path for a single net, each routed net will form obstacles for later paths. This will add complexities to route later nets and make its routing longer than the optimal path or sometimes impossible to complete. Today’s sequential routing often applies heuristic method to further refine the solution. Through this process, all nets will be rerouted in different order to improve the quality of routing. Because of this, we are motivated to apply simulated annealing, one of the metaheuristic methods to our routing model to produce better candidates of sequence.

Crosshole Tomography, Waveform Inversion, and Anisotropy: A Combined Approach Using Simulated Annealing

Science.gov (United States)