S-parameter uncertainty computations
DEFF Research Database (Denmark)
Vidkjær, Jens
1993-01-01
A method for computing uncertainties of measured s-parameters is presented. Unlike the specification software provided with network analyzers, the new method is capable of calculating the uncertainties of arbitrary s-parameter sets and instrument settings.......A method for computing uncertainties of measured s-parameters is presented. Unlike the specification software provided with network analyzers, the new method is capable of calculating the uncertainties of arbitrary s-parameter sets and instrument settings....
Computing with Epistemic Uncertainty
2015-01-01
modified the input uncertainties in any way. And by avoiding the need for simulation, various assumptions and selection of specific sampling...strategies that may affect results are also avoided . According with the Principle of Maximum Uncertainty , epistemic intervals represent the highest input...
Symbolic computation for evaluation of measurement uncertainty
Wei, P.; Yang, QP; Salleh; Jones, BE
2007-01-01
In recent years, with the rapid development of symbolic computation, the integration of symbolic and numeric methods is increasingly applied in various applications. This paper proposed the use of symbolic computation for the evaluation of measurement uncertainty. The general method and procedure are discussed, and its great potential and powerful features for measurement uncertainty evaluation has been demonstrated through examples.
Uncertainty and error in computational simulations
Energy Technology Data Exchange (ETDEWEB)
Oberkampf, W.L.; Diegert, K.V.; Alvin, K.F.; Rutherford, B.M.
1997-10-01
The present paper addresses the question: ``What are the general classes of uncertainty and error sources in complex, computational simulations?`` This is the first step of a two step process to develop a general methodology for quantitatively estimating the global modeling and simulation uncertainty in computational modeling and simulation. The second step is to develop a general mathematical procedure for representing, combining and propagating all of the individual sources through the simulation. The authors develop a comprehensive view of the general phases of modeling and simulation. The phases proposed are: conceptual modeling of the physical system, mathematical modeling of the system, discretization of the mathematical model, computer programming of the discrete model, numerical solution of the model, and interpretation of the results. This new view is built upon combining phases recognized in the disciplines of operations research and numerical solution methods for partial differential equations. The characteristics and activities of each of these phases is discussed in general, but examples are given for the fields of computational fluid dynamics and heat transfer. They argue that a clear distinction should be made between uncertainty and error that can arise in each of these phases. The present definitions for uncertainty and error are inadequate and. therefore, they propose comprehensive definitions for these terms. Specific classes of uncertainty and error sources are then defined that can occur in each phase of modeling and simulation. The numerical sources of error considered apply regardless of whether the discretization procedure is based on finite elements, finite volumes, or finite differences. To better explain the broad types of sources of uncertainty and error, and the utility of their categorization, they discuss a coupled-physics example simulation.
Uncertainty in biology a computational modeling approach
Gomez-Cabrero, David
2016-01-01
Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate stude...
Efficient Quantification of Uncertainties in Complex Computer Code Results Project
National Aeronautics and Space Administration — Propagation of parameter uncertainties through large computer models can be very resource intensive. Frameworks and tools for uncertainty quantification are...
Modelling of data uncertainties on hybrid computers
Energy Technology Data Exchange (ETDEWEB)
Schneider, Anke (ed.)
2016-06-15
The codes d{sup 3}f and r{sup 3}t are well established for modelling density-driven flow and nuclide transport in the far field of repositories for hazardous material in deep geological formations. They are applicable in porous media as well as in fractured rock or mudstone, for modelling salt- and heat transport as well as a free groundwater surface. Development of the basic framework of d{sup 3}f and r{sup 3}t had begun more than 20 years ago. Since that time significant advancements took place in the requirements for safety assessment as well as for computer hardware development. The period of safety assessment for a repository of high-level radioactive waste was extended to 1 million years, and the complexity of the models is steadily growing. Concurrently, the demands on accuracy increase. Additionally, model and parameter uncertainties become more and more important for an increased understanding of prediction reliability. All this leads to a growing demand for computational power that requires a considerable software speed-up. An effective way to achieve this is the use of modern, hybrid computer architectures which requires basically the set-up of new data structures and a corresponding code revision but offers a potential speed-up by several orders of magnitude. The original codes d{sup 3}f and r{sup 3}t were applications of the software platform UG /BAS 94/ whose development had begun in the early nineteennineties. However, UG had recently been advanced to the C++ based, substantially revised version UG4 /VOG 13/. To benefit also in the future from state-of-the-art numerical algorithms and to use hybrid computer architectures, the codes d{sup 3}f and r{sup 3}t were transferred to this new code platform. Making use of the fact that coupling between different sets of equations is natively supported in UG4, d{sup 3}f and r{sup 3}t were combined to one conjoint code d{sup 3}f++. A direct estimation of uncertainties for complex groundwater flow models with the
Efficient uncertainty quantification in computational fluid dynamics
Loeven, G.J.A.
2010-01-01
When modeling physical systems, several sources of uncertainty are present. For example, variability in boundary conditions like free stream velocity or ambient pressure are always present. Furthermore, uncertainties in geometry arise from production tolerances, wear or unknown deformations under lo
Methodology for characterizing modeling and discretization uncertainties in computational simulation
Energy Technology Data Exchange (ETDEWEB)
ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.
2000-03-01
This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.
A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics
Witteveen, J.A.S.; Bijl, H.
2006-01-01
A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equ
Environmental engineering calculations involving uncertainties; either in the model itself or in the data, are far beyond the capabilities of conventional analysis for any but the simplest of models. There exist a number of general-purpose computer simulation languages, using Mon...
Propagation of Computational Uncertainty Using the Modern Design of Experiments
DeLoach, Richard
2007-01-01
This paper describes the use of formally designed experiments to aid in the error analysis of a computational experiment. A method is described by which the underlying code is approximated with relatively low-order polynomial graduating functions represented by truncated Taylor series approximations to the true underlying response function. A resource-minimal approach is outlined by which such graduating functions can be estimated from a minimum number of case runs of the underlying computational code. Certain practical considerations are discussed, including ways and means of coping with high-order response functions. The distributional properties of prediction residuals are presented and discussed. A practical method is presented for quantifying that component of the prediction uncertainty of a computational code that can be attributed to imperfect knowledge of independent variable levels. This method is illustrated with a recent assessment of uncertainty in computational estimates of Space Shuttle thermal and structural reentry loads attributable to ice and foam debris impact on ascent.
Uncertainty quantification in computational fluid dynamics and aircraft engines
Montomoli, Francesco; D'Ammaro, Antonio; Massini, Michela; Salvadori, Simone
2015-01-01
This book introduces novel design techniques developed to increase the safety of aircraft engines. The authors demonstrate how the application of uncertainty methods can overcome problems in the accurate prediction of engine lift, caused by manufacturing error. This in turn ameliorates the difficulty of achieving required safety margins imposed by limits in current design and manufacturing methods. This text shows that even state-of-the-art computational fluid dynamics (CFD) are not able to predict the same performance measured in experiments; CFD methods assume idealised geometries but ideal geometries do not exist, cannot be manufactured and their performance differs from real-world ones. By applying geometrical variations of a few microns, the agreement with experiments improves dramatically, but unfortunately the manufacturing errors in engines or in experiments are unknown. In order to overcome this limitation, uncertainty quantification considers the probability density functions of manufacturing errors...
Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics
Zhu, J. Z.; He, Guowei; Bushnell, Dennis M. (Technical Monitor)
2002-01-01
Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. Verification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically provide quantitative information about the reliability of the numerical simulation by estimating numerical approximation error, computational model induced errors and the uncertainties contained in the mathematical models so that the reliability of the numerical simulation can be verified and validated. We also propose to develop and implement methodologies and techniques that can control the error and uncertainty during the numerical simulation so that the reliability of the numerical simulation can be improved.
A High Performance Bayesian Computing Framework for Spatiotemporal Uncertainty Modeling
Cao, G.
2015-12-01
All types of spatiotemporal measurements are subject to uncertainty. With spatiotemporal data becomes increasingly involved in scientific research and decision making, it is important to appropriately model the impact of uncertainty. Quantitatively modeling spatiotemporal uncertainty, however, is a challenging problem considering the complex dependence and dataheterogeneities.State-space models provide a unifying and intuitive framework for dynamic systems modeling. In this paper, we aim to extend the conventional state-space models for uncertainty modeling in space-time contexts while accounting for spatiotemporal effects and data heterogeneities. Gaussian Markov Random Field (GMRF) models, also known as conditional autoregressive models, are arguably the most commonly used methods for modeling of spatially dependent data. GMRF models basically assume that a geo-referenced variable primarily depends on its neighborhood (Markov property), and the spatial dependence structure is described via a precision matrix. Recent study has shown that GMRFs are efficient approximation to the commonly used Gaussian fields (e.g., Kriging), and compared with Gaussian fields, GMRFs enjoy a series of appealing features, such as fast computation and easily accounting for heterogeneities in spatial data (e.g, point and areal). This paper represents each spatial dataset as a GMRF and integrates them into a state-space form to statistically model the temporal dynamics. Different types of spatial measurements (e.g., categorical, count or continuous), can be accounted for by according link functions. A fast alternative to MCMC framework, so-called Integrated Nested Laplace Approximation (INLA), was adopted for model inference.Preliminary case studies will be conducted to showcase the advantages of the described framework. In the first case, we apply the proposed method for modeling the water table elevation of Ogallala aquifer over the past decades. In the second case, we analyze the
Computational uncertainty principle in nonlinear ordinary differential equations
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The error propagation for general numerical method in ordinarydifferential equations ODEs is studied. Three kinds of convergence, theoretical, numerical and actual convergences, are presented. The various components of round-off error occurring in floating-point computation are fully detailed. By introducing a new kind of recurrent inequality, the classical error bounds for linear multistep methods are essentially improved, and joining probabilistic theory the “normal” growth of accumulated round-off error is derived. Moreover, a unified estimate for the total error of general method is given. On the basis of these results, we rationally interpret the various phenomena found in the numerical experiments in part I of this paper and derive two universal relations which are independent of types of ODEs, initial values and numerical schemes and are consistent with the numerical results. Furthermore, we give the explicitly mathematical expression of the computational uncertainty principle and expound the intrinsic relation between two uncertainties which result from the inaccuracies of numerical method and calculating machine.
Efficient Quantification of Uncertainties in Complex Computer Code Results Project
National Aeronautics and Space Administration — This proposal addresses methods for efficient quantification of margins and uncertainties (QMU) for models that couple multiple, large-scale commercial or...
The Uncertainty Test for the MAAP Computer Code
Energy Technology Data Exchange (ETDEWEB)
Park, S. H.; Song, Y. M.; Park, S. Y.; Ahn, K. I.; Kim, K. R.; Lee, Y. J. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2008-10-15
After the Three Mile Island Unit 2 (TMI-2) and Chernobyl accidents, safety issues for a severe accident are treated in various aspects. Major issues in our research part include a level 2 PSA. The difficulty in expanding the level 2 PSA as a risk information activity is the uncertainty. In former days, it attached a weight to improve the quality in a internal accident PSA, but the effort is insufficient for decrease the phenomenon uncertainty in the level 2 PSA. In our country, the uncertainty degree is high in the case of a level 2 PSA model, and it is necessary to secure a model to decrease the uncertainty. We have not yet experienced the uncertainty assessment technology, the assessment system itself depends on advanced nations. In advanced nations, the severe accident simulator is implemented in the hardware level. But in our case, basic function in a software level can be implemented. In these circumstance at home and abroad, similar instances are surveyed such as UQM and MELCOR. Referred to these instances, SAUNA (Severe Accident UNcertainty Analysis) system is being developed in our project to assess and decrease the uncertainty in a level 2 PSA. It selects the MAAP code to analyze the uncertainty in a severe accident.
Fuzzy randomness uncertainty in civil engineering and computational mechanics
Möller, Bernd
2004-01-01
This book, for the first time, provides a coherent, overall concept for taking account of uncertainty in the analysis, the safety assessment, and the design of structures. The reader is introduced to the problem of uncertainty modeling and familiarized with particular uncertainty models. For simultaneously considering stochastic and non-stochastic uncertainty the superordinated uncertainty model fuzzy randomness, which contains real valued random variables as well as fuzzy variables as special cases, is presented. For this purpose basic mathematical knowledge concerning the fuzzy set theory and the theory of fuzzy random variables is imparted. The body of the book comprises the appropriate quantification of uncertain structural parameters, the fuzzy and fuzzy probabilistic structural analysis, the fuzzy probabilistic safety assessment, and the fuzzy cluster structural design. The completely new algorithms are described in detail and illustrated by way of demonstrative examples.
Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty.
Sankaran, Sethuraman; Grady, Leo; Taylor, Charles A
2015-12-01
Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Accurate coronary segmentation and realistic physiologic modeling of boundary conditions are important steps to ensure a high diagnostic performance. Segmentation of the coronary arteries can be constructed by a combination of automated algorithms with human review and editing. However, blood pressure and flow are not impacted equally by different local sections of the coronary artery tree. Focusing human review and editing towards regions that will most affect the subsequent simulations can significantly accelerate the review process. We define geometric sensitivity as the standard deviation in hemodynamics-derived metrics due to uncertainty in lumen segmentation. We develop a machine learning framework for estimating the geometric sensitivity in real time. Features used include geometric and clinical variables, and reduced-order models. We develop an anisotropic kernel regression method for assessment of lumen narrowing score, which is used as a feature in the machine learning algorithm. A multi-resolution sensitivity algorithm is introduced to hierarchically refine regions of high sensitivity so that we can quantify sensitivities to a desired spatial resolution. We show that the mean absolute error of the machine learning algorithm compared to 3D simulations is less than 0.01. We further demonstrate that sensitivity is not predicted simply by anatomic reduction but also encodes information about hemodynamics which in turn depends on downstream boundary conditions. This sensitivity approach can be extended to other systems such as cerebral flow, electro-mechanical simulations, etc.
Computational uncertainty principle in nonlinear ordinary differential equations
Institute of Scientific and Technical Information of China (English)
LI; Jianping
2001-01-01
［1］Li Jianping, Zeng Qingcun, Chou Jifan, Computational Uncertainty Principle in Nonlinear Ordinary Differential Equations I. Numerical Results, Science in China, Ser. E, 2000, 43(5): 449［2］Henrici, P., Discrete Variable Methods in Ordinary Differential Equations, New York: John Wiley, 1962, 1; 187.［3］Henrici, P., Error Propagation for Difference Methods, New York: John Whiley, 1963.［4］Gear, C. W., Numerical Initial Value Problems in Ordinary Differential Equations, Englewood Cliffs, NJ: Prentice-Hall, 1971, 1; 72.［5］Hairer, E., Nrsett, S. P., Wanner, G., Solving Ordinary Differential Equations I. Nonstiff Problems, 2nd ed., Berlin-Heidelberg-New York: Springer-Verlag, 1993, 130.［6］Stoer, J., Bulirsch, R., Introduction to Numerical Analysis, 2nd ed., Vol. 1, Berlin-Heidelberg-New York: Springer-Verlag (reprinted in China by Beijing Wold Publishing Corporation), 1998, 428.［7］Li Qingyang, Numerical Methods in Ordinary Differential Equations (Stiff Problems and Boundary Value Problems), in Chinese Beijing: Higher Education Press, 1991, 1.［8］Li Ronghua, Weng Guochen, Numerical Methods in Differential Equations (in Chinese), 3rd ed., Beijing: Higher Education Press, 1996, 1.［9］Dahlquist, G., Convergence and stability in the numerical integration of ordinary differential equations, Math. Scandinavica, 1956, 4: 33.［10］Dahlquist, G., 33 years of numerical instability, Part I, BIT, 1985, 25: 188.［11］Heisenberg, W., The Physical Principles of Quantum Theory, Chicago: University of Chicago Press, 1930.［12］McMurry, S. M., Quantum Mechanics, London: Addison-Wesley Longman Ltd (reprined in China by Beijing World Publishing Corporation), 1998.
Binding in light nuclei: Statistical NN uncertainties vs Computational accuracy
Perez, R Navarro; Amaro, J E; Arriola, E Ruiz
2016-01-01
We analyse the impact of the statistical uncertainties of the the nucleon-nucleon interaction, based on the Granada-2013 np-pp database, on the binding energies of the triton and the alpha particle using a bootstrap method, by solving the Faddeev equations for $^3$H and the Yakubovsky equations for $^4$He respectively. We check that in practice about 30 samples prove enough for a reliable error estimate. An extrapolation of the well fulfilled Tjon-line correlation predicts the experimental binding of the alpha particle within uncertainties.
Nguyen, Hung T; Wu, Berlin; Xiang, Gang
2012-01-01
In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area. Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy. This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to in...
DEFF Research Database (Denmark)
Müller, Pavel; Hiller, Jochen; Dai, Y.;
2014-01-01
This paper presents the application of the substitution method for the estimation of measurement uncertainties using calibrated workpieces in X-ray computed tomography (CT) metrology. We have shown that this, well accepted method for uncertainty estimation using tactile coordinate measuring...
Groves, Curtis; Ilie, Marcel; Schallhorn, Paul
2014-01-01
Spacecraft components may be damaged due to airflow produced by Environmental Control Systems (ECS). There are uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field around a spacecraft from the ECS System. This paper describes an approach to estimate the uncertainty in using CFD to predict the airflow speeds around an encapsulated spacecraft.
Binding in light nuclei: Statistical NN uncertainties vs Computational accuracy
Navarro Pérez, R.; Nogga, A.; Amaro, J. E.; Ruiz Arriola, E.
2016-08-01
We analyse the impact of the statistical uncertainties of the the nucleon-nucleon interaction, based on the Granada-2013 np-pp database, on the binding energies of the triton and the alpha particle using a bootstrap method, by solving the Faddeev equations for 3H and the Yakubovsky equations for 4He respectively. We check that in practice about 30 samples prove enough for a reliable error estimate. An extrapolation of the well fulfilled Tjon-line correlation predicts the experimental binding of the alpha particle within uncertainties. Presented by RNP at Workshop for young scientists with research interests focused on physics at FAIR 14-19 February 2016 Garmisch-Partenkirchen (Germany).
Computational methods estimating uncertainties for profile reconstruction in scatterometry
Gross, H.; Rathsfeld, A.; Scholze, F.; Model, R.; Bär, M.
2008-04-01
The solution of the inverse problem in scatterometry, i.e. the determination of periodic surface structures from light diffraction patterns, is incomplete without knowledge of the uncertainties associated with the reconstructed surface parameters. With decreasing feature sizes of lithography masks, increasing demands on metrology techniques arise. Scatterometry as a non-imaging indirect optical method is applied to periodic line-space structures in order to determine geometric parameters like side-wall angles, heights, top and bottom widths and to evaluate the quality of the manufacturing process. The numerical simulation of the diffraction process is based on the finite element solution of the Helmholtz equation. The inverse problem seeks to reconstruct the grating geometry from measured diffraction patterns. Restricting the class of gratings and the set of measurements, this inverse problem can be reformulated as a non-linear operator equation in Euclidean spaces. The operator maps the grating parameters to the efficiencies of diffracted plane wave modes. We employ a Gauss-Newton type iterative method to solve this operator equation and end up minimizing the deviation of the measured efficiency or phase shift values from the simulated ones. The reconstruction properties and the convergence of the algorithm, however, is controlled by the local conditioning of the non-linear mapping and the uncertainties of the measured efficiencies or phase shifts. In particular, the uncertainties of the reconstructed geometric parameters essentially depend on the uncertainties of the input data and can be estimated by various methods. We compare the results obtained from a Monte Carlo procedure to the estimations gained from the approximative covariance matrix of the profile parameters close to the optimal solution and apply them to EUV masks illuminated by plane waves with wavelengths in the range of 13 nm.
Establishing performance requirements of computer based systems subject to uncertainty
Energy Technology Data Exchange (ETDEWEB)
Robinson, D.
1997-02-01
An organized systems design approach is dictated by the increasing complexity of computer based systems. Computer based systems are unique in many respects but share many of the same problems that have plagued design engineers for decades. The design of complex systems is difficult at best, but as a design becomes intensively dependent on the computer processing of external and internal information, the design process quickly borders chaos. This situation is exacerbated with the requirement that these systems operate with a minimal quantity of information, generally corrupted by noise, regarding the current state of the system. Establishing performance requirements for such systems is particularly difficult. This paper briefly sketches a general systems design approach with emphasis on the design of computer based decision processing systems subject to parameter and environmental variation. The approach will be demonstrated with application to an on-board diagnostic (OBD) system for automotive emissions systems now mandated by the state of California and the Federal Clean Air Act. The emphasis is on an approach for establishing probabilistically based performance requirements for computer based systems.
Groves, Curtis Edward
2014-01-01
Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions
Groves, Curtis Edward
2014-01-01
Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics
DEFF Research Database (Denmark)
Hiller, Jochen; Genta, Gianfranco; Barbato, Giulio
2014-01-01
Industrial applications of computed tomography (CT) for dimensional metrology on various components are fast increasing, owing to a number of favorable properties such as capability of non-destructive internal measurements. Uncertainty evaluation is however more complex than in conventional...... the problem concerning measurement uncertainties was addressed with bootstrap and successfully applied to ball-bar CT measurements. Results obtained enabled extension to more complex shapes such as actual industrial components as we show by tests on a hollow cylinder workpiece....
Assessment of uncertainties of the models used in thermal-hydraulic computer codes
Gricay, A. S.; Migrov, Yu. A.
2015-09-01
The article deals with matters concerned with the problem of determining the statistical characteristics of variable parameters (the variation range and distribution law) in analyzing the uncertainty and sensitivity of calculation results to uncertainty in input data. A comparative analysis of modern approaches to uncertainty in input data is presented. The need to develop an alternative method for estimating the uncertainty of model parameters used in thermal-hydraulic computer codes, in particular, in the closing correlations of the loop thermal hydraulics block, is shown. Such a method shall feature the minimal degree of subjectivism and must be based on objective quantitative assessment criteria. The method includes three sequential stages: selecting experimental data satisfying the specified criteria, identifying the key closing correlation using a sensitivity analysis, and carrying out case calculations followed by statistical processing of the results. By using the method, one can estimate the uncertainty range of a variable parameter and establish its distribution law in the above-mentioned range provided that the experimental information is sufficiently representative. Practical application of the method is demonstrated taking as an example the problem of estimating the uncertainty of a parameter appearing in the model describing transition to post-burnout heat transfer that is used in the thermal-hydraulic computer code KORSAR. The performed study revealed the need to narrow the previously established uncertainty range of this parameter and to replace the uniform distribution law in the above-mentioned range by the Gaussian distribution law. The proposed method can be applied to different thermal-hydraulic computer codes. In some cases, application of the method can make it possible to achieve a smaller degree of conservatism in the expert estimates of uncertainties pertinent to the model parameters used in computer codes.
Computer-assisted uncertainty assessment of k0-NAA measurement results
Bučar, T.; Smodiš, B.
2008-10-01
In quantifying measurement uncertainty of measurement results obtained by the k0-based neutron activation analysis ( k0-NAA), a number of parameters should be considered and appropriately combined in deriving the final budget. To facilitate this process, a program ERON (ERror propagatiON) was developed, which computes uncertainty propagation factors from the relevant formulae and calculates the combined uncertainty. The program calculates uncertainty of the final result—mass fraction of an element in the measured sample—taking into account the relevant neutron flux parameters such as α and f, including their uncertainties. Nuclear parameters and their uncertainties are taken from the IUPAC database (V.P. Kolotov and F. De Corte, Compilation of k0 and related data for NAA). Furthermore, the program allows for uncertainty calculations of the measured parameters needed in k0-NAA: α (determined with either the Cd-ratio or the Cd-covered multi-monitor method), f (using the Cd-ratio or the bare method), Q0 (using the Cd-ratio or internal comparator method) and k0 (using the Cd-ratio, internal comparator or the Cd subtraction method). The results of calculations can be printed or exported to text or MS Excel format for further analysis. Special care was taken to make the calculation engine portable by having possibility of its incorporation into other applications (e.g., DLL and WWW server). Theoretical basis and the program are described in detail, and typical results obtained under real measurement conditions are presented.
Computational Fluid Dynamics Uncertainty Analysis Applied to Heat Transfer over a Flat Plate
Groves, Curtis Edward; Ilie, Marcel; Schallhorn, Paul A.
2013-01-01
There have been few discussions on using Computational Fluid Dynamics (CFD) without experimental validation. Pairing experimental data, uncertainty analysis, and analytical predictions provides a comprehensive approach to verification and is the current state of the art. With pressed budgets, collecting experimental data is rare or non-existent. This paper investigates and proposes a method to perform CFD uncertainty analysis only from computational data. The method uses current CFD uncertainty techniques coupled with the Student-T distribution to predict the heat transfer coefficient over a at plate. The inputs to the CFD model are varied from a specified tolerance or bias error and the difference in the results are used to estimate the uncertainty. The variation in each input is ranked from least to greatest to determine the order of importance. The results are compared to heat transfer correlations and conclusions drawn about the feasibility of using CFD without experimental data. The results provide a tactic to analytically estimate the uncertainty in a CFD model when experimental data is unavailable
Interpolation Method Needed for Numerical Uncertainty Analysis of Computational Fluid Dynamics
Groves, Curtis; Ilie, Marcel; Schallhorn, Paul
2014-01-01
Using Computational Fluid Dynamics (CFD) to predict a flow field is an approximation to the exact problem and uncertainties exist. There is a method to approximate the errors in CFD via Richardson's Extrapolation. This method is based off of progressive grid refinement. To estimate the errors in an unstructured grid, the analyst must interpolate between at least three grids. This paper describes a study to find an appropriate interpolation scheme that can be used in Richardson's extrapolation or other uncertainty method to approximate errors. Nomenclature
Angelikopoulos, Panagiotis; Papadimitriou, Costas; Koumoutsakos, Petros
2012-10-01
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon.
Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González
2016-01-01
Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.
DEFF Research Database (Denmark)
Hiller, Jochen; Reindl, Leonard M
2012-01-01
into account the main error sources for the measurement. This method has the potential to deal with all kinds of systematic and random errors that influence a dimensional CT measurement. A case study demonstrates the practical application of the VCT simulator using numerically generated CT data and statistical......The knowledge of measurement uncertainty is of great importance in conformance testing in production. The tolerance limit for production must be reduced by the amounts of measurement uncertainty to ensure that the parts are in fact within the tolerance. Over the last 5 years, industrial X......-ray computed tomography (CT) has become an important technology for dimensional quality control. In this paper a computer simulation platform is presented which is able to investigate error sources in dimensional CT measurements. The typical workflow in industrial CT metrology is described and methods...
Prediction and Uncertainty in Computational Modeling of Complex Phenomena: A Whitepaper
Energy Technology Data Exchange (ETDEWEB)
Trucano, T.G.
1999-01-20
This report summarizes some challenges associated with the use of computational science to predict the behavior of complex phenomena. As such, the document is a compendium of ideas that have been generated by various staff at Sandia. The report emphasizes key components of the use of computational to predict complex phenomena, including computational complexity and correctness of implementations, the nature of the comparison with data, the importance of uncertainty quantification in comprehending what the prediction is telling us, and the role of risk in making and using computational predictions. Both broad and more narrowly focused technical recommendations for research are given. Several computational problems are summarized that help to illustrate the issues we have emphasized. The tone of the report is informal, with virtually no mathematics. However, we have attempted to provide a useful bibliography that would assist the interested reader in pursuing the content of this report in greater depth.
Groves, Curtis E.; Ilie, marcel; Shallhorn, Paul A.
2014-01-01
Computational Fluid Dynamics (CFD) is the standard numerical tool used by Fluid Dynamists to estimate solutions to many problems in academia, government, and industry. CFD is known to have errors and uncertainties and there is no universally adopted method to estimate such quantities. This paper describes an approach to estimate CFD uncertainties strictly numerically using inputs and the Student-T distribution. The approach is compared to an exact analytical solution of fully developed, laminar flow between infinite, stationary plates. It is shown that treating all CFD input parameters as oscillatory uncertainty terms coupled with the Student-T distribution can encompass the exact solution.
Hilbert's Second Problems and Uncertainty Computing, from HCP Logic's Point of View
Institute of Scientific and Technical Information of China (English)
James Kuodo Huang
2006-01-01
Hilbert's complete perfect (HCP) logic is introduced. The G(o)del's incompleteness theorem discloses the limit of logic. Huang's universal consistent theorem and relative consistent theorem extends the limit of logic. The proofs of these theorems are in 2-valued logic but the completeness can be extended in the three-valued HCP logic.The author proposes HCP logic for the foundation of uncertainty computing as well.
A novel method for the evaluation of uncertainty in dose volume histogram computation
Cutanda-Henriquez, Francisco
2007-01-01
Dose volume histograms are a useful tool in state-of-the-art radiotherapy planning, and it is essential to be aware of their limitations. Dose distributions computed by treatment planning systems are affected by several sources of uncertainty such as algorithm limitations, measurement uncertainty in the data used to model the beam and residual differences between measured and computed dose, once the model is optimized. In order to take into account the effect of uncertainty, a probabilistic approach is proposed and a new kind of histogram, a dose-expected volume histogram, is introduced. The expected value of the volume in the region of interest receiving an absorbed dose equal or greater than a certain value is found using the probability distribution of the dose at each point. A rectangular probability distribution is assumed for this point dose, and a relationship is given for practical computations. This method is applied to a set of dose volume histograms for different regions of interest for 6 brain pat...
Flood risk assessment at the regional scale: Computational challenges and the monster of uncertainty
Efstratiadis, Andreas; Papalexiou, Simon-Michael; Markonis, Yiannis; Koukouvinos, Antonis; Vasiliades, Lampros; Papaioannou, George; Loukas, Athanasios
2016-04-01
We present a methodological framework for flood risk assessment at the regional scale, developed within the implementation of the EU Directive 2007/60 in Greece. This comprises three phases: (a) statistical analysis of extreme rainfall data, resulting to spatially-distributed parameters of intensity-duration-frequency (IDF) relationships and their confidence intervals, (b) hydrological simulations, using event-based semi-distributed rainfall-runoff approaches, and (c) hydraulic simulations, employing the propagation of flood hydrographs across the river network and the mapping of inundated areas. The flood risk assessment procedure is employed over the River Basin District of Thessaly, Greece, which requires schematization and modelling of hundreds of sub-catchments, each one examined for several risk scenarios. This is a challenging task, involving multiple computational issues to handle, such as the organization, control and processing of huge amount of hydrometeorological and geographical data, the configuration of model inputs and outputs, and the co-operation of several software tools. In this context, we have developed supporting applications allowing massive data processing and effective model coupling, thus drastically reducing the need for manual interventions and, consequently, the time of the study. Within flood risk computations we also account for three major sources of uncertainty, in an attempt to provide upper and lower confidence bounds of flood maps, i.e. (a) statistical uncertainty of IDF curves, (b) structural uncertainty of hydrological models, due to varying anteceded soil moisture conditions, and (c) parameter uncertainty of hydraulic models, with emphasis to roughness coefficients. Our investigations indicate that the combined effect of the above uncertainties (which are certainly not the unique ones) result to extremely large bounds of potential inundation, thus rising many questions about the interpretation and usefulness of current flood
Energy Technology Data Exchange (ETDEWEB)
Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)
2006-10-01
Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.
Whalen, Scott; Lee, Choonsik; Williams, Jonathan L.; Bolch, Wesley E.
2008-01-01
Current efforts to reconstruct organ doses in children undergoing diagnostic imaging or therapeutic interventions using ionizing radiation typically rely upon the use of reference anthropomorphic computational phantoms coupled to Monte Carlo radiation transport codes. These phantoms are generally matched to individual patients based upon nearest age or sometimes total body mass. In this study, we explore alternative methods of phantom-to-patient matching with the goal of identifying those methods which yield the lowest residual errors in internal organ volumes. Various thoracic and abdominal organs were segmented and organ volumes obtained from chest-abdominal-pelvic (CAP) computed tomography (CT) image sets from 38 pediatric patients ranging in age from 2 months to 15 years. The organs segmented included the skeleton, heart, kidneys, liver, lungs and spleen. For each organ, least-squared regression lines, 95th percentile confidence intervals and 95th percentile prediction intervals were established as a function of patient age, trunk volume, estimated trunk mass, trunk height, and three estimates of the ventral body cavity volume based on trunk height alone, or in combination with circumferential, width and/or breadth measurements in the mid-chest of the patient. When matching phantom to patient based upon age, residual uncertainties in organ volumes ranged from 53% (lungs) to 33% (kidneys), and when trunk mass was used (surrogate for total body mass as we did not have images of patient head, arms or legs), these uncertainties ranged from 56% (spleen) to 32% (liver). When trunk height is used as the matching parameter, residual uncertainties in organ volumes were reduced to between 21 and 29% for all organs except the spleen (40%). In the case of the lungs and skeleton, the two-fold reduction in organ volume uncertainties was seen in moving from patient age to trunk height—a parameter easily measured in the clinic. When ventral body cavity volumes were used
Energy Technology Data Exchange (ETDEWEB)
Whalen, Scott [Department of Nuclear and Radiological Engineering, University of Florida, Gainesville, FL 32611 (United States); Lee, Choonsik [Department of Nuclear and Radiological Engineering, University of Florida, Gainesville, FL 32611 (United States); Williams, Jonathan L [Department of Radiology, University of Florida, Gainesville, FL 32611 (United States); Bolch, Wesley E [Departments of Nuclear and Radiological and Biomedical Engineering, University of Florida, Gainesville, FL 32611 (United States)
2008-01-21
Current efforts to reconstruct organ doses in children undergoing diagnostic imaging or therapeutic interventions using ionizing radiation typically rely upon the use of reference anthropomorphic computational phantoms coupled to Monte Carlo radiation transport codes. These phantoms are generally matched to individual patients based upon nearest age or sometimes total body mass. In this study, we explore alternative methods of phantom-to-patient matching with the goal of identifying those methods which yield the lowest residual errors in internal organ volumes. Various thoracic and abdominal organs were segmented and organ volumes obtained from chest-abdominal-pelvic (CAP) computed tomography (CT) image sets from 38 pediatric patients ranging in age from 2 months to 15 years. The organs segmented included the skeleton, heart, kidneys, liver, lungs and spleen. For each organ, least-squared regression lines, 95th percentile confidence intervals and 95th percentile prediction intervals were established as a function of patient age, trunk volume, estimated trunk mass, trunk height, and three estimates of the ventral body cavity volume based on trunk height alone, or in combination with circumferential, width and/or breadth measurements in the mid-chest of the patient. When matching phantom to patient based upon age, residual uncertainties in organ volumes ranged from 53% (lungs) to 33% (kidneys), and when trunk mass was used (surrogate for total body mass as we did not have images of patient head, arms or legs), these uncertainties ranged from 56% (spleen) to 32% (liver). When trunk height is used as the matching parameter, residual uncertainties in organ volumes were reduced to between 21 and 29% for all organs except the spleen (40%). In the case of the lungs and skeleton, the two-fold reduction in organ volume uncertainties was seen in moving from patient age to trunk height-a parameter easily measured in the clinic. When ventral body cavity volumes were used
Quantifying the Contribution of Post-Processing in Computed Tomography Measurement Uncertainty
DEFF Research Database (Denmark)
Stolfi, Alessandro; Thompson, Mary Kathryn; Carli, Lorenzo;
2016-01-01
This paper evaluates and quantifies the repeatability of post-processing settings, such as surface determination, data fitting, and the definition of the datum system, on the uncertainties of Computed Tomography (CT) measurements. The influence of post-processing contributions was determined...... by calculating the standard deviation of 10 repeated measurement evaluations on the same data set. The evaluations were performed on an industrial assembly. Each evaluation includes several dimensional and geometrical measurands that were expected to have different responses to the various post......-processing settings. It was found that the definition of the datum system had the largest impact on the uncertainty with a standard deviation of a few microns. The surface determination and data fitting had smaller contributions with sub-micron repeatability....
Energy Technology Data Exchange (ETDEWEB)
Miller, David C. [U.S. DOE; Ng, Brenda [Lawrence Livermore National Laboratory; Eslick, John [Carnegie Mellon University
2014-01-01
Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and universities that is developing, demonstrating, and deploying a suite of multi-scale modeling and simulation tools. One significant computational tool is FOQUS, a Framework for Optimization and Quantification of Uncertainty and Sensitivity, which enables basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to rapidly synthesize and optimize a process and determine the level of uncertainty associated with the resulting process. The overall approach of CCSI is described with a more detailed discussion of FOQUS and its application to carbon capture systems.
Elishakoff, I.; Sarlin, N.
2016-06-01
In this paper we provide a general methodology of analysis and design of systems involving uncertainties. Available experimental data is enclosed by some geometric figures (triangle, rectangle, ellipse, parallelogram, super ellipse) of minimum area. Then these areas are inflated resorting to the Chebyshev inequality in order to take into account the forecasted data. Next step consists in evaluating response of system when uncertainties are confined to one of the above five suitably inflated geometric figures. This step involves a combined theoretical and computational analysis. We evaluate the maximum response of the system subjected to variation of uncertain parameters in each hypothesized region. The results of triangular, interval, ellipsoidal, parallelogram, and super ellipsoidal calculi are compared with the view of identifying the region that leads to minimum of maximum response. That response is identified as a result of the suggested predictive inference. The methodology thus synthesizes probabilistic notion with each of the five calculi. Using the term "pillar" in the title was inspired by the News Release (2013) on according Honda Prize to J. Tinsley Oden, stating, among others, that "Dr. Oden refers to computational science as the "third pillar" of scientific inquiry, standing beside theoretical and experimental science. Computational science serves as a new paradigm for acquiring knowledge and informing decisions important to humankind". Analysis of systems with uncertainties necessitates employment of all three pillars. The analysis is based on the assumption that that the five shapes are each different conservative estimates of the true bounding region. The smallest of the maximal displacements in x and y directions (for a 2D system) therefore provides the closest estimate of the true displacements based on the above assumption.
Energy Technology Data Exchange (ETDEWEB)
Hadjidoukas, P.E.; Angelikopoulos, P. [Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 (Switzerland); Papadimitriou, C. [Department of Mechanical Engineering, University of Thessaly, GR-38334 Volos (Greece); Koumoutsakos, P., E-mail: petros@ethz.ch [Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 (Switzerland)
2015-03-01
We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.
Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.
2015-03-01
We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.
Akhtar, T.; Shoemaker, C. A.
2011-12-01
Assessing the sensitivity of calibration results to different calibration criteria can be done through multi objective optimization that considers multiple calibration criteria. This analysis can be extended to uncertainty analysis by comparing the results of simulation of the model with parameter sets from many points along a Pareto Front. In this study we employ multi-objective optimization in order to understand which parameter values should be used for flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville Reservoir in upstate New York. The comprehensive analysis procedure encapsulates identification of suitable objectives, analysis of trade-offs obtained through multi-objective optimization, and the impact of the trade-offs uncertainty. Examples of multiple criteria can include a) quality of the fit in different seasons, b) quality of the fit for high flow events and for low flow events, c) quality of the fit for different constituents (e.g. water versus nutrients). Many distributed watershed models are computationally expensive and include a large number of parameters that are to be calibrated. Efficient optimization algorithms are hence needed to find good solutions to multi-criteria calibration problems in a feasible amount of time. We apply a new algorithm called Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS), for efficient multi-criteria optimization of the Cannonsville SWAT watershed calibration problem. GOMORS is a stochastic optimization method, which makes use of Radial Basis Functions for approximation of the computationally expensive objectives. GOMORS performance is also compared against other multi-objective algorithms ParEGO and NSGA-II. ParEGO is a kriging based efficient multi-objective optimization algorithm, whereas NSGA-II is a well-known multi-objective evolutionary optimization algorithm. GOMORS is more efficient than both ParEGO and NSGA-II in providing
Gualdrini, G; Tanner, R J; Agosteo, S; Pola, A; Bedogni, R; Ferrari, P; Lacoste, V; Bordy, J-M; Chartier, J-L; de Carlan, L; Gomez Ros, J-M; Grosswendt, B; Kodeli, I; Price, R A; Rollet, S; Schultz, F; Siebert, B; Terrissol, M; Zankl, M
2008-01-01
Within the scope of CONRAD (A Coordinated Action for Radiation Dosimetry) Work Package 4 on Computational Dosimetry jointly collaborated with the other research actions on internal dosimetry, complex mixed radiation fields at workplaces and medical staff dosimetry. Besides these collaborative actions, WP4 promoted an international comparison on eight problems with their associated experimental data. A first set of three problems, the results of which are herewith summarised, dealt only with the expression of the stochastic uncertainties of the results: the analysis of the response function of a proton recoil telescope detector, the study of a Bonner sphere neutron spectrometer and the analysis of the neutron spectrum and dosimetric quantity H(p)(10) in a thermal neutron facility operated by IRSN Cadarache (the SIGMA facility). A second paper will summarise the results of the other five problems which dealt with the full uncertainty budget estimate. A third paper will present the results of a comparison on in vivo measurements of the (241)Am bone-seeker nuclide distributed in the knee. All the detailed papers will be presented in the WP4 Final Workshop Proceedings.
Computational uncertainty principle in nonlinear ordinary differential equations--Numerical results
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In a majority of cases of long-time numerical integration for initial-value problems, round-off error has received little attention. Using twenty-nine numerical methods, the influence of round-off error on numerical solutions is generally studied through a large number of numerical experiments. Here we find that there exists a strong dependence on machine precision (which is a new kind of dependence different from the sensitive dependence on initial conditions), maximally effective computation time (MECT) and optimal stepsize (OS) in solving nonlinear ordinary differential equations (ODEs) in finite machine precision. And an optimal searching method for evaluating MECT and OS under finite machine precision is presented. The relationships between MECT, OS, the order of numerical method and machine precision are found. Numerical results show that round-off error plays a significant role in the above phenomena. Moreover, we find two universal relations which are independent of the types of ODEs, initial values and numerical schemes. Based on the results of numerical experiments, we present a computational uncertainty principle, which is a great challenge to the reliability of long-time numerical integration for nonlinear ODEs.
Solomon, Gemma C; Reimers, Jeffrey R; Hush, Noel S
2005-06-01
In the calculation of conduction through single molecule's approximations about the geometry and electronic structure of the system are usually made in order to simplify the problem. Previously [G. C. Solomon, J. R. Reimers, and N. S. Hush, J. Chem. Phys. 121, 6615 (2004)], we have shown that, in calculations employing cluster models for the electrodes, proper treatment of the open-shell nature of the clusters is the most important computational feature required to make the results sensitive to variations in the structural and chemical features of the system. Here, we expand this and establish a general hierarchy of requirements involving treatment of geometrical approximations. These approximations are categorized into two classes: those associated with finite-dimensional methods for representing the semi-infinite electrodes, and those associated with the chemisorption topology. We show that ca. 100 unique atoms are required in order to properly characterize each electrode: using fewer atoms leads to nonsystematic variations in conductivity that can overwhelm the subtler changes. The choice of binding site is shown to be the next most important feature, while some effects that are difficult to control experimentally concerning the orientations at each binding site are actually shown to be insignificant. Verification of this result provides a general test for the precision of computational procedures for molecular conductivity. Predictions concerning the dependence of conduction on substituent and other effects on the central molecule are found to be meaningful only when they exceed the uncertainties of the effects associated with binding-site variation.
Directory of Open Access Journals (Sweden)
Elaine eDuffin
2014-02-01
Full Text Available Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts. We reporthere findings that extend existing knowledge and show that a modified reinforcement learningmodel which differentiates between prior reward and punishment can provide the best fit tohuman behaviour in decision making under uncertainty. More specifically, we examined thefit of our modified reinforcement learning model to human behavioural data in a probabilistictwo-alternative decision making task with rule reversals. Our results demonstrate that this modelpredicted human behaviour better than a series of other models based on reinforcement learningor Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning modeldoes not include any representation of rule switches. When our task is considered purely as amachine learning task, to gain as many rewards as possible without trying to describe humanbehaviour, the performance of modified reinforcement learning and Bayesian methods is similar.Others have used various computational models to describe human behaviour in similar tasks,however, we are not aware of any who have compared Bayesian reasoning with reinforcementlearning modified to differentiate rewards and punishments.
Energy Technology Data Exchange (ETDEWEB)
Reutter, Bryan W.; Gullberg, Grant T.; Huesman, Ronald H.
2001-04-09
The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of
Directory of Open Access Journals (Sweden)
Nerea Mangado
2016-11-01
Full Text Available Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.
Farahmand, Touraj; Hamilton, Stuart
2016-04-01
Application of the index velocity method for computing continuous records of discharge has become increasingly common, especially since the introduction of low-cost acoustic Doppler velocity meters (ADVMs). In general, the index velocity method can be used at locations where stage-discharge methods are used, but it is especially appropriate and recommended when more than one specific discharge can be measured for a specific stage such as backwater and unsteady flow conditions caused by but not limited to the following; stream confluences, streams flowing into lakes or reservoirs, tide-affected streams, regulated streamflows (dams or control structures), or streams affected by meteorological forcing, such as strong prevailing winds. In existing index velocity modeling techniques, two models (ratings) are required; index velocity model and stage-area model. The outputs from each of these models, mean channel velocity (Vm) and cross-sectional area (A), are then multiplied together to compute a discharge. Mean channel velocity (Vm) can generally be determined by a multivariate regression parametric model such as linear regression in the simplest case. The main challenges in the existing index velocity modeling techniques are; 1) Preprocessing and QA/QC of continuous index velocity data and synchronizing them with discharge measurements. 2) Nonlinear relationship between mean velocity and index velocity which is not uncommon at monitoring locations. 3)Model exploration and analysis in order to find the optimal regression model predictor(s) and model type (linear vs nonlinear and if nonlinear number of the parameters). 3) Model changes caused by dynamical changes in the environment (geomorphic, biological) over time 5) Deployment of the final model into the Data Management Systems (DMS) for real-time discharge calculation 6) Objective estimation of uncertainty caused by: field measurement errors; structural uncertainty; parameter uncertainty; and continuous sensor data
Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts
2015-07-01
in catchment models. 1. Evaluating parameter uncertainty. Water Resources Research 19(5):1151–1172. Lee, P. M. 2012. Bayesian statistics: An...confidence or belief. For example, in a Bayesian framework, someone could say that tomorrow’s weather has a 50% chance of rain . Whereas in a...frequentist framework, someone can only say that there is a certain probably of rain for a given day of the year based on the historical record. Parameters
Aleatory Uncertainty and Scale Effects in Computational Damage Models for Failure and Fragmentation
2014-09-01
theoretical limit load if its heterogeneities (holes and pits) are large enough to induce non- infinitesimal perturbations in the stress field. If mesh...from the symmetry axis or excessive cracking in a deterministic simulation such as Figure 3(a). Round-off noise represents a numeri- cally infinitesimal ... calculations , making the implementation of aleatory uncertainty quite manageable. The P safe.T; V / function allows the effects of micromorphology to be
Directory of Open Access Journals (Sweden)
Cristian Toma
2013-01-01
Full Text Available This study presents wavelets-computational aspects of Sterian-realistic approach to uncertainty principle in high energy physics. According to this approach, one cannot make a device for the simultaneous measuring of the canonical conjugate variables in reciprocal Fourier spaces. However, such aspects regarding the use of conjugate Fourier spaces can be also noticed in quantum field theory, where the position representation of a quantum wave is replaced by momentum representation before computing the interaction in a certain point of space, at a certain moment of time. For this reason, certain properties regarding the switch from one representation to another in these conjugate Fourier spaces should be established. It is shown that the best results can be obtained using wavelets aspects and support macroscopic functions for computing (i wave-train nonlinear relativistic transformation, (ii reflection/refraction with a constant shift, (iii diffraction considered as interaction with a null phase shift without annihilation of associated wave, (iv deflection by external electromagnetic fields without phase loss, and (v annihilation of associated wave-train through fast and spatially extended phenomena according to uncertainty principle.
Ortman, Robert L.; Carr, Domenic A.; James, Ryan; Long, Daniel; O'Shaughnessy, Matthew R.; Valenta, Christopher R.; Tuell, Grady H.
2016-05-01
We have developed a prototype real-time computer for a bathymetric lidar capable of producing point clouds attributed with total propagated uncertainty (TPU). This real-time computer employs a "mixed-mode" architecture comprised of an FPGA, CPU, and GPU. Noise reduction and ranging are performed in the digitizer's user-programmable FPGA, and coordinates and TPU are calculated on the GPU. A Keysight M9703A digitizer with user-programmable Xilinx Virtex 6 FPGAs digitizes as many as eight channels of lidar data, performs ranging, and delivers the data to the CPU via PCIe. The floating-point-intensive coordinate and TPU calculations are performed on an NVIDIA Tesla K20 GPU. Raw data and computed products are written to an SSD RAID, and an attributed point cloud is displayed to the user. This prototype computer has been tested using 7m-deep waveforms measured at a water tank on the Georgia Tech campus, and with simulated waveforms to a depth of 20m. Preliminary results show the system can compute, store, and display about 20 million points per second.
Xuan, Y.; Mahinthakumar, K.; Arumugam, S.; DeCarolis, J.
2015-12-01
Owing to the lack of a consistent approach to assimilate probabilistic forecasts for water and energy systems, utilization of climate forecasts for conjunctive management of these two systems is very limited. Prognostic management of these two systems presents a stochastic co-optimization problem that seeks to determine reservoir releases and power allocation strategies while minimizing the expected operational costs subject to probabilistic climate forecast constraints. To address these issues, we propose a high performance computing (HPC) enabled computational framework for stochastic co-optimization of water and energy resource allocations under climate uncertainty. The computational framework embodies a new paradigm shift in which attributes of climate (e.g., precipitation, temperature) and its forecasted probability distribution are employed conjointly to inform seasonal water availability and electricity demand. The HPC enabled cyberinfrastructure framework is developed to perform detailed stochastic analyses, and to better quantify and reduce the uncertainties associated with water and power systems management by utilizing improved hydro-climatic forecasts. In this presentation, our stochastic multi-objective solver extended from Optimus (Optimization Methods for Universal Simulators), is introduced. The solver uses parallel cooperative multi-swarm method to solve for efficient solution of large-scale simulation-optimization problems on parallel supercomputers. The cyberinfrastructure harnesses HPC resources to perform intensive computations using ensemble forecast models of streamflow and power demand. The stochastic multi-objective particle swarm optimizer we developed is used to co-optimize water and power system models under constraints over a large number of ensembles. The framework sheds light on the application of climate forecasts and cyber-innovation framework to improve management and promote the sustainability of water and energy systems.
Chen, Hsinchun; Martinez, Joanne; Kirchhoff, Amy; Ng, Tobun D.; Schatz, Bruce R.
1998-01-01
Grounded on object filtering, automatic indexing, and co-occurrence analysis, an experiment was performed using a parallel supercomputer to analyze over 400,000 abstracts in an INSPEC computer engineering collection. A user evaluation revealed that system-generated thesauri were better than the human-generated INSPEC subject thesaurus in concept…
Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography.
Grbic, Sasa; Easley, Thomas F; Mansi, Tommaso; Bloodworth, Charles H; Pierce, Eric L; Voigt, Ingmar; Neumann, Dominik; Krebs, Julian; Yuh, David D; Jensen, Morten O; Comaniciu, Dorin; Yoganathan, Ajit P
2017-01-01
Intervention planning is essential for successful Mitral Valve (MV) repair procedures. Finite-element models (FEM) of the MV could be used to achieve this goal, but the translation to the clinical domain is challenging. Many input parameters for the FEM models, such as tissue properties, are not known. In addition, only simplified MV geometry models can be extracted from non-invasive modalities such as echocardiography imaging, lacking major anatomical details such as the complex chordae topology. A traditional approach for FEM computation is to use a simplified model (also known as parachute model) of the chordae topology, which connects the papillary muscle tips to the free-edges and select basal points. Building on the existing parachute model a new and comprehensive MV model was developed that utilizes a novel chordae representation capable of approximating regional connectivity. In addition, a fully automated personalization approach was developed for the chordae rest length, removing the need for tedious manual parameter selection. Based on the MV model extracted during mid-diastole (open MV) the MV geometric configuration at peak systole (closed MV) was computed according to the FEM model. In this work the focus was placed on validating MV closure computation. The method is evaluated on ten in vitro ovine cases, where in addition to echocardiography imaging, high-resolution μCT imaging is available for accurate validation.
Institute of Scientific and Technical Information of China (English)
MENG Guo-jie; REN Jin-wei; WU Ji-cang; SHEN Xu-hui
2008-01-01
Based on Taylor series expansion and strain components expressions of elastic mechanics, we derive formulae of strain and rotation tensor for small arrays in spherical coordinates system. By linearization process of the formulae, we also derive expressions of strain components and Euler vector uncertainties respectively for subnets using the law of error propagation. Taking GPS velocity field in Sichuan-Yunnan area as an example, we compute dilation rate and maximum shear strain rate field using the above procedure, and their characteristics are preliminarily carried on. Limits of the strain model for small array are also discussed. We make detailed explanations on small array method and the choice of small arrays. How to set weights of GPS observations are further discussed. Moreover relationship between strain and radius of GPS subnets is also analyzed.
Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen
2006-01-01
This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a
Riely, Amelia; Sablan, Kyle; Xiaotao, Thomas; Furst, Jacob; Raicu, Daniela
2015-03-01
Medical imaging technology has always provided radiologists with the opportunity to view and keep records of anatomy of the patient. With the development of machine learning and intelligent computing, these images can be used to create Computer-Aided Diagnosis (CAD) systems, which can assist radiologists in analyzing image data in various ways to provide better health care to patients. This paper looks at increasing accuracy and reducing cost in creating CAD systems, specifically in predicting the malignancy of lung nodules in the Lung Image Database Consortium (LIDC). Much of the cost in creating an accurate CAD system stems from the need for multiple radiologist diagnoses or annotations of each image, since there is rarely a ground truth diagnosis and even different radiologists' diagnoses of the same nodule often disagree. To resolve this issue, this paper outlines an method of selective iterative classification that predicts lung nodule malignancy by using multiple radiologist diagnoses only for cases that can benefit from them. Our method achieved 81% accuracy while costing only 46% of the method that indiscriminately used all annotations, which achieved a lower accuracy of 70%, while costing more.
Kong, Song-Charng; Reitz, Rolf D.
2003-06-01
This study used a numerical model to investigate the combustion process in a premixed iso-octane homogeneous charge compression ignition (HCCI) engine. The engine was a supercharged Cummins C engine operated under HCCI conditions. The CHEMKIN code was implemented into an updated KIVA-3V code so that the combustion could be modelled using detailed chemistry in the context of engine CFD simulations. The model was able to accurately simulate the ignition timing and combustion phasing for various engine conditions. The unburned hydrocarbon emissions were also well predicted while the carbon monoxide emissions were under predicted. Model results showed that the majority of unburned hydrocarbon is located in the piston-ring crevice region and the carbon monoxide resides in the vicinity of the cylinder walls. A sensitivity study of the computational grid resolution indicated that the combustion predictions were relatively insensitive to the grid density. However, the piston-ring crevice region needed to be simulated with high resolution to obtain accurate emissions predictions. The model results also indicated that HCCI combustion and emissions are very sensitive to the initial mixture temperature. The computations also show that the carbon monoxide emissions prediction can be significantly improved by modifying a key oxidation reaction rate constant.
Institute of Scientific and Technical Information of China (English)
李建平[1; 曾庆存[2; 丑纪范[3
2000-01-01
In a majority of cases of long-time numerical integration for initial-value problems, roundoff error has received little attention. Using twenty-nine numerical methods, the influence of round-off error on numerical solutions is generally studied through a large number of numerical experiments. Here we find that there exists a strong dependence on machine precision (which is a new kind of dependence different from the sensitive dependence on initial conditions), maximally effective computation time (MECT) and optimal stepsize (OS) in solving nonlinear ordinary differential equations (ODEs) in finite machine precision. And an optimal searching method for evaluating MECT and OS under finite machine precision is presented. The relationships between MECT, OS, the order of numerical method and machine precision are found. Numerical results show that round-off error plays a significant role in the above phenomena. Moreover, we find two universal relations which are independent of the types of ODEs, initial val
Uncertainties in radiative transfer computations: consequences on the ocean color products
Dilligeard, Eric; Zagolski, Francis; Fischer, Juergen; Santer, Richard P.
2003-05-01
Operational MERIS (MEdium Resolution Imaging Spectrometer) level-2 processing uses auxiliary data generated by two radiative transfer tools. These two codes simulate upwelling radiances within a coupled 'Atmosphere-Ocean' system, using different approaches based on the matrix-operator method (MOMO) and the successive orders (SO) technique. Intervalidation of these two radiative transfer codes was performed in order to implement them in the MERIS level-2 processing. MOMO and SO simulations were then conducted on a set of representative test cases. Results stressed both for all test cases good agreements were observed. The scattering processes are retrieved within a few tenths of a percent. Nevertheless, some substantial discrepancies occurred if the polarization is not taken into account mainly in the Rayleigh scattering computations. A preliminary study indicates that the impact of the code inaccuracy in the water leaving radiances retrieval (a level-2 MERIS product) is large, up to 50% in relative difference. Applying the OC2 algorithm, the effect on the retrieval chlorophyll concentration is less than 10%.
Directory of Open Access Journals (Sweden)
Milen Radell
2016-08-01
Full Text Available Recent work has found that personality factors that confer vulnerability to addiction can also affect learning and economic decision making. One personality trait which has been implicated in vulnerability to addiction is intolerance to uncertainty (IU, i.e. a preference for familiar over unknown (possible better options. In animals, the motivation to obtain drugs is often assessed through conditioned place preference (CPP, which compares preference for contexts where drug reward was previously received. It is an open question whether participants with high IU also show heightened preference for previously-rewarded contexts. To address this question, we developed a novel computer-based CPP task for humans in which participants guide an avatar through a paradigm in which one room contains frequent reward and one contains less frequent reward. Following exposure to both contexts, subjects are assessed for preference to enter the previously-rich and previously-poor room. Individuals with low IU showed little bias to enter the previously-rich room first, and instead entered both rooms at about the same rate. By contrast, those with high IU showed a strong bias to enter the previously-rich room first. This suggests an increased tendency to chase reward in the intolerant group, consistent with previously observed behavior in opioid-addicted individuals. Thus, high IU may represent a pre-existing cognitive bias that provides a mechanism to promote decision-making processes that increase vulnerability to addiction.
Energy Technology Data Exchange (ETDEWEB)
Andres, T.H
2002-05-01
This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)
DEFF Research Database (Denmark)
Müller, Pavel; Hiller, Jochen; Cantatore, Angela
2012-01-01
measurement results using different measuring strategies applied in different inspection software packages for volume and surface data analysis. The strategy influence is determined by calculating the measurement uncertainty. This investigation includes measurements of two industrial items, an aluminium pipe...
Uncertainty and Cognitive Control
Directory of Open Access Journals (Sweden)
Faisal eMushtaq
2011-10-01
Full Text Available A growing trend of neuroimaging, behavioural and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1 There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2 There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3 The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the need for control; (4 Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders.
Energy Technology Data Exchange (ETDEWEB)
Thomas, R.E.
1982-03-01
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software.
DEFF Research Database (Denmark)
Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan
assessment of novel working fluids relies on accurate property data. Flammability data like the lower and upper flammability limit (LFL and UFL) play an important role in quantifying the risk of fire and explosion. For novel working fluid candidates experimental values are not available for the safety...... analysis. In this case property prediction models like group contribution (GC) models can estimate flammability data. The estimation needs to be accurate, reliable and as less time consuming as possible [1]. However, GC property prediction methods frequently lack rigorous uncertainty analysis. Hence...
Bartley, David; Lidén, Göran
2008-08-01
The reporting of measurement uncertainty has recently undergone a major harmonization whereby characteristics of a measurement method obtained during establishment and application are combined componentwise. For example, the sometimes-pesky systematic error is included. A bias component of uncertainty can be often easily established as the uncertainty in the bias. However, beyond simply arriving at a value for uncertainty, meaning to this uncertainty if needed can sometimes be developed in terms of prediction confidence in uncertainty-based intervals covering what is to be measured. To this end, a link between concepts of accuracy and uncertainty is established through a simple yet accurate approximation to a random variable known as the non-central Student's t-distribution. Without a measureless and perpetual uncertainty, the drama of human life would be destroyed. Winston Churchill.
Lindley, Dennis V
2013-01-01
Praise for the First Edition ""...a reference for everyone who is interested in knowing and handling uncertainty.""-Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
DEFF Research Database (Denmark)
Persson, Gitte Fredberg; Nygaard, Ditte Eklund; Af Rosenschöld, Per Munck;
2011-01-01
PURPOSE: Artifacts impacting the imaged tumor volume can be seen in conventional three-dimensional CT (3DCT) scans for planning of lung cancer radiotherapy but can be reduced with the use of respiration-correlated imaging, i.e., 4DCT or breathhold CT (BHCT) scans. The aim of this study...... was to compare delineated gross tumor volume (GTV) sizes in 3DCT, 4DCT, and BHCT scans of patients with lung tumors. METHODS AND MATERIALS: A total of 36 patients with 46 tumors referred for stereotactic radiotherapy of lung tumors were included. All patients underwent positron emission tomography (PET)/CT, 4DCT......, and BHCT scans. GTVs in all CT scans of individual patients were delineated during one session by a single physician to minimize systematic delineation uncertainty. The GTV size from the BHCT was considered the closest to true tumor volume and was chosen as the reference. The reference GTV size...
Energy Technology Data Exchange (ETDEWEB)
Liu Baoding [Tsinghua Univ., Beijing (China). Uncertainty Theory Lab.
2007-07-01
Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, and countable subadditivity axioms. The goal of uncertainty theory is to study the behavior of uncertain phenomena such as fuzziness and randomness. The main topics include probability theory, credibility theory, and chance theory. For this new edition the entire text has been totally rewritten. More importantly, the chapters on chance theory and uncertainty theory are completely new. This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, industrial engineering, computer science, artificial intelligence, and management science will find this work a stimulating and useful reference. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Alonso, M.; Sanz, J.; Rodriguez, A.; Falquina, R. [Universidad Nacional de Educacion a Distancia (UNED), Dept. of Power Engineering, Madrid (Spain); Cabellos, O.; Sanz, J. [Universidad Politecnica de Madrid, Instituto de Fusion Nuclear (UPM) (Spain)
2003-07-01
The feasibility of nuclear fusion as a realistic option for energy generation depends on its radioactive waste management assessment. In this respect, the production of high level waste is to be avoided and the reduction of low level waste volumes is to be enhanced. Three different waste management options are commonly regarded in fusion plants: Hands-on Recycling, Remote Recycling and Shallow Land Burial (SLB). Therefore, important research work has been undertaken to find low activation structural materials. In performing this task, a major issue is to compute the concentration limits (CLs) for all natural elements, which will be used to select the intended constituent elements of a particular Low Activation Material (LAM) and assess how much the impurities can deteriorate the waste management properties. Nevertheless, the reliable computation of CLs depends on the accuracy of nuclear data (mainly activation cross-sections) and the suitability of the computational method both for inertial and magnetic fusion environments. In this paper the importance of nuclear data uncertainties and mathematical algorithms used in different activation calculations for waste management purposes will be studied. Our work is centred on the study of {sup 186}W activation under first structural wall conditions of Hylife-II inertial fusion reactor design. The importance of the dominant transmutation/decay sequence has been documented in several publications. From a practical point of view, W is used in low activation materials for fusion applications: Cr-W ferritic/martensitic steels, and the need to better compute its activation has been assessed, in particular in relation to the cross-section uncertainties for reactions leading to Ir isotopes. {sup 192n}Ir and {sup 192}Ir reach a secular equilibrium, and {sup 192n}Ir is the critical one for waste management, with a half life of 241 years. From a theoretical point of view, this is one of the most complex chains appearing in
Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan
2015-01-01
Computer Aided Molecular Design (CAMD) is an important tool to generate, test and evaluate promising chemical products. CAMD can be used in thermodynamic cycle for the design of pure component or mixture working fluids in order to improve the heat transfer capacity of the system. The safety assessment of novel working fluids relies on accurate property data. Flammability data like the lower and upper flammability limit (LFL and UFL) play an important role in quantifying the risk of fire and e...
Energy Technology Data Exchange (ETDEWEB)
Burr, T., E-mail: tburr@lanl.gov [Los Alamos National Laboratory, Los Alamos, NM (United States); Hoover, A. [Los Alamos National Laboratory, Los Alamos, NM (United States); Croft, S. [Oak Ridge National Laboratory, Oak Ridge, TN (United States); Rabin, M. [Los Alamos National Laboratory, Los Alamos, NM (United States)
2015-01-15
High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced X-rays and gamma-rays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signal-to-background ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and half-lives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.
García-Jaramillo, Maira; Calm, Remei; Bondia, Jorge; Tarín, Cristina; Vehí, Josep
2009-01-01
Objective The objective of this article was to develop a methodology to quantify the risk of suffering different grades of hypo- and hyperglycemia episodes in the postprandial state. Methods Interval predictions of patient postprandial glucose were performed during a 5-hour period after a meal for a set of 3315 scenarios. Uncertainty in the patient's insulin sensitivities and carbohydrate (CHO) contents of the planned meal was considered. A normalized area under the curve of the worst-case predicted glucose excursion for severe and mild hypo- and hyperglycemia glucose ranges was obtained and weighted accordingly to their importance. As a result, a comprehensive risk measure was obtained. A reference model of preprandial glucose values representing the behavior in different ranges was chosen by a ξ2 test. The relationship between the computed risk index and the probability of occurrence of events was analyzed for these reference models through 19,500 Monte Carlo simulations. Results The obtained reference models for each preprandial glucose range were 100, 160, and 220 mg/dl. A relationship between the risk index ranges 120 and the probability of occurrence of mild and severe postprandial hyper- and hypoglycemia can be derived. Conclusions When intrapatient variability and uncertainty in the CHO content of the meal are considered, a safer prediction of possible hyper- and hypoglycemia episodes induced by the tested insulin therapy can be calculated. PMID:20144339
Energy Technology Data Exchange (ETDEWEB)
Kim, Moo Hwan; Seo, Kyoung Woo [POSTECH, Pohang (Korea, Republic of)
2001-03-15
In the probability approach, the calculated CCFPs of all the scenarios were zero, which meant that it was expected that for all the accident scenarios the maximum pressure load induced by DCH was lower than the containment failure pressure obtained from the fragility curve. Thus, it can be stated that the KSNP containment is robust to the DCH threat. And uncertainty of computer codes used to be two (deterministic and probabilistic) approaches were reduced by the sensitivity tests and the research with the verification and comparison of the DCH models in each code. So, this research was to evaluate synthetic result of DCH issue and expose accurate methodology to assess containment integrity about operating PWR in Korea.
Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe
2015-04-01
In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representation of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.
Energy Technology Data Exchange (ETDEWEB)
Kersaudy, Pierric, E-mail: pierric.kersaudy@orange.com [Orange Labs, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France); Whist Lab, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France); ESYCOM, Université Paris-Est Marne-la-Vallée, 5 boulevard Descartes, 77700 Marne-la-Vallée (France); Sudret, Bruno [ETH Zürich, Chair of Risk, Safety and Uncertainty Quantification, Stefano-Franscini-Platz 5, 8093 Zürich (Switzerland); Varsier, Nadège [Orange Labs, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France); Whist Lab, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France); Picon, Odile [ESYCOM, Université Paris-Est Marne-la-Vallée, 5 boulevard Descartes, 77700 Marne-la-Vallée (France); Wiart, Joe [Orange Labs, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France); Whist Lab, 38 avenue du Général Leclerc, 92130 Issy-les-Moulineaux (France)
2015-04-01
In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representation of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.
Sciacchitano, Andrea; Wieneke, Bernhard
2016-08-01
This paper discusses the propagation of the instantaneous uncertainty of PIV measurements to statistical and instantaneous quantities of interest derived from the velocity field. The expression of the uncertainty of vorticity, velocity divergence, mean value and Reynolds stresses is derived. It is shown that the uncertainty of vorticity and velocity divergence requires the knowledge of the spatial correlation between the error of the x and y particle image displacement, which depends upon the measurement spatial resolution. The uncertainty of statistical quantities is often dominated by the random uncertainty due to the finite sample size and decreases with the square root of the effective number of independent samples. Monte Carlo simulations are conducted to assess the accuracy of the uncertainty propagation formulae. Furthermore, three experimental assessments are carried out. In the first experiment, a turntable is used to simulate a rigid rotation flow field. The estimated uncertainty of the vorticity is compared with the actual vorticity error root-mean-square, with differences between the two quantities within 5-10% for different interrogation window sizes and overlap factors. A turbulent jet flow is investigated in the second experimental assessment. The reference velocity, which is used to compute the reference value of the instantaneous flow properties of interest, is obtained with an auxiliary PIV system, which features a higher dynamic range than the measurement system. Finally, the uncertainty quantification of statistical quantities is assessed via PIV measurements in a cavity flow. The comparison between estimated uncertainty and actual error demonstrates the accuracy of the proposed uncertainty propagation methodology.
Model Uncertainty for Bilinear Hysteric Systems
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Thoft-Christensen, Palle
In structural reliability analysis at least three types of uncertainty must be considered, namely physical uncertainty, statistical uncertainty, and model uncertainty (see e.g. Thoft-Christensen & Baker [1]). The physical uncertainty is usually modelled by a number of basic variables by predictive...... density functions, Veneziano [2]. In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis is related to the concept of a failure surface (or limit state surface) in the n-dimension basic variable space then model...... uncertainty is at least due to the neglected variables, the modelling of the failure surface and the computational technique used....
Introduction to uncertainty quantification
Sullivan, T J
2015-01-01
Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantification are defined, and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favourite problems to understand their strengths and weaknesses, also making the text suitable as a self-study. This text is designed as an introduction to uncertainty quantification for senior undergraduate and graduate students with a mathematical or statistical back...
Liu, Baoding
2015-01-01
When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...
Sensitivity and uncertainty analysis
Cacuci, Dan G; Navon, Ionel Michael
2005-01-01
As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative c
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas
2002-01-01
Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration unce...
Calibration Under Uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Swiler, Laura Painton; Trucano, Timothy Guy
2005-03-01
This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.
Uncertainty Quantification in Aeroelasticity
Beran, Philip; Stanford, Bret; Schrock, Christopher
2017-01-01
Physical interactions between a fluid and structure, potentially manifested as self-sustained or divergent oscillations, can be sensitive to many parameters whose values are uncertain. Of interest here are aircraft aeroelastic interactions, which must be accounted for in aircraft certification and design. Deterministic prediction of these aeroelastic behaviors can be difficult owing to physical and computational complexity. New challenges are introduced when physical parameters and elements of the modeling process are uncertain. By viewing aeroelasticity through a nondeterministic prism, where key quantities are assumed stochastic, one may gain insights into how to reduce system uncertainty, increase system robustness, and maintain aeroelastic safety. This article reviews uncertainty quantification in aeroelasticity using traditional analytical techniques not reliant on computational fluid dynamics; compares and contrasts this work with emerging methods based on computational fluid dynamics, which target richer physics; and reviews the state of the art in aeroelastic optimization under uncertainty. Barriers to continued progress, for example, the so-called curse of dimensionality, are discussed.
DEFF Research Database (Denmark)
Nguyen, Daniel Xuyen
This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models...... the high rate of exit seen in the first years of exporting. Finally, when faced with multiple countries in which to export, some firms will choose to sequentially export in order to slowly learn more about its chances for success in untested markets....
Orbital State Uncertainty Realism
Horwood, J.; Poore, A. B.
2012-09-01
Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten
A monomial chaos approach for efficient uncertainty quantification on nonlinear problems
Witteveen, J.A.S.; Bijl, H.
2008-01-01
A monomial chaos approach is presented for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equation
Uncertainty in Air Quality Modeling.
Fox, Douglas G.
1984-01-01
Under the direction of the AMS Steering Committee for the EPA Cooperative Agreement on Air Quality Modeling, a small group of scientists convened to consider the question of uncertainty in air quality modeling. Because the group was particularly concerned with the regulatory use of models, its discussion focused on modeling tall stack, point source emissions.The group agreed that air quality model results should be viewed as containing both reducible error and inherent uncertainty. Reducible error results from improper or inadequate meteorological and air quality data inputs, and from inadequacies in the models. Inherent uncertainty results from the basic stochastic nature of the turbulent atmospheric motions that are responsible for transport and diffusion of released materials. Modelers should acknowledge that all their predictions to date contain some associated uncertainty and strive also to quantify uncertainty.How can the uncertainty be quantified? There was no consensus from the group as to precisely how uncertainty should be calculated. One subgroup, which addressed statistical procedures, suggested that uncertainty information could be obtained from comparisons of observations and predictions. Following recommendations from a previous AMS workshop on performance evaluation (Fox. 1981), the subgroup suggested construction of probability distribution functions from the differences between observations and predictions. Further, they recommended that relatively new computer-intensive statistical procedures be considered to improve the quality of uncertainty estimates for the extreme value statistics of interest in regulatory applications.A second subgroup, which addressed the basic nature of uncertainty in a stochastic system, also recommended that uncertainty be quantified by consideration of the differences between observations and predictions. They suggested that the average of the difference squared was appropriate to isolate the inherent uncertainty that
Slangen, A.B.A.; van de Wal, R.S.W.
2011-01-01
A large part of present-day sea-level change is formed by the melt of glaciers and ice caps (GIC). This study focuses on the uncertainties in the calculation of the GIC contribution on a century timescale. The model used is based on volume-area scaling, 5 combined with the mass balance sensitivity o
The parameters uncertainty inflation fallacy
Pernot, Pascal
2016-01-01
Statistical estimation of the prediction uncertainty of physical models is typically hindered by the inadequacy of these models due to various approximations they are built upon. The prediction errors due to model inadequacy can be handled either by correcting the model's results, or by adapting the model's parameters uncertainty to generate prediction uncertainty representative, in a way to be defined, of model inadequacy errors. The main advantage of the latter approach is its transferability to the prediction of other quantities of interest based on the same parameters. A critical review of state-of-the-art implementations of this approach in computational chemistry shows that it is biased, in the sense that it does not produce prediction uncertainty bands conforming with model inadequacy errors.
Institute of Scientific and Technical Information of China (English)
许鑫; 吕震宙; 罗晓鹏
2011-01-01
The probability density function integral of Borgonovo input uncertainty importance measure is hard to calculate. Hereby based on the definition of the input uncertainty importance measure, the concept of norm is introduced into the uncertainty importance ranking analysis for the first time, and a new importance ranking computational strategy is developed, for which some equivalent norms are selected.This strategy replaces the integral by its equivalent norm, and introduces a kind of regularization computing method for uncertainty importance measure at the same time, which has more applicability. Theoretically,this strategy can provide many kinds equivalent norms for estimating the uncertainty importance ranking.Comparisons of present work with Borgonovo method and Liu method subsequently show that the new method is the easiest one. Finally, two examples are illustrated the feasibility of the present work.%针对Borgonovo基本输入变量重要度中概率密度函数积分不易求解的缺陷,基于重要度定义将范数的概念引入灵敏度分析中的重要度排序分析领域,提出一种新的基本输入变量重要度排序的计算策略,并为该计算策略选择了若干等价范数,从而避免求解积分的困难.该方法将所求积分用其等价范数进行替代,同时采用适用性更加广泛的一种正则化基本输入变量重要度的计算方法.从理论上讲,该计算策略能够为重要度排序计算提供多种等价范数形式.从所提计算策略与Borgonovo方法、Liu方法的比较可以看出:所提方法是三种方法中最简单有效的.最后,给出两个算例来说明所提方法的可行性.
Uncertainty quantification and error analysis
Energy Technology Data Exchange (ETDEWEB)
Higdon, Dave M [Los Alamos National Laboratory; Anderson, Mark C [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Klein, Richard [Los Alamos National Laboratory; Berliner, Mark [OHIO STATE UNIV.; Covey, Curt [LLNL; Ghattas, Omar [UNIV OF TEXAS; Graziani, Carlo [UNIV OF CHICAGO; Seager, Mark [LLNL; Sefcik, Joseph [LLNL; Stark, Philip [UC/BERKELEY; Stewart, James [SNL
2010-01-01
UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.
Generalized Uncertainty Principle and Angular Momentum
Bosso, Pasquale
2016-01-01
Various models of quantum gravity suggest a modification of the Heisenberg's Uncertainty Principle, to the so-called Generalized Uncertainty Principle, between position and momentum. In this work we show how this modification influences the theory of angular momentum in Quantum Mechanics. In particular, we compute Planck scale corrections to angular momentum eigenvalues, the Hydrogen atom spectrum, the Stern-Gerlach experiment and the Clebsch-Gordan coefficients. We also examine effects of the Generalized Uncertainty Principle on multi-particle systems.
Fuzzy Uncertainty Evaluation for Fault Tree Analysis
Energy Technology Data Exchange (ETDEWEB)
Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)
2015-05-15
This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.
Uncertainties in Site Amplification Estimation
Cramer, C. H.; Bonilla, F.; Hartzell, S.
2004-12-01
Typically geophysical profiles (layer thickness, velocity, density, Q) and dynamic soil properties (modulus and damping versus strain curves) are used with appropriate input ground motions in a soil response computer code to estimate site amplification. Uncertainties in observations can be used to generate a distribution of possible site amplifications. The biggest sources of uncertainty in site amplifications estimates are the uncertainties in (1) input ground motions, (2) shear-wave velocities (Vs), (3) dynamic soil properties, (4) soil response code used, and (5) dynamic pore pressure effects. A study of site amplification was conducted for the 1 km thick Mississippi embayment sediments beneath Memphis, Tennessee (see USGS OFR 04-1294 on the web). In this study, the first three sources of uncertainty resulted in a combined coefficient of variation of 10 to 60 percent. The choice of soil response computer program can lead to uncertainties in median estimates of +/- 50 percent. Dynamic pore pressure effects due to the passing of seismic waves in saturated soft sediments are normally not considered in site-amplification studies and can contribute further large uncertainties in site amplification estimates. The effects may range from dilatancy and high-frequency amplification (such as observed at some sites during the 1993 Kushiro-Oki, Japan and 2001 Nisqually, Washington earthquakes) or general soil failure and deamplification of ground motions (such as observed at Treasure Island during the 1989 Loma Prieta, California earthquake). Examples of two case studies using geotechnical data for downhole arrays in Kushiro, Japan and the Wildlife Refuge, California using one dynamic code, NOAH, will be presented as examples of modeling uncertainties associated with these effects. Additionally, an example of inversion for estimates of in-situ dilatancy-related geotechnical modeling parameters will be presented for the Kushiro, Japan site.
Uncertainty and Engagement with Learning Games
Howard-Jones, Paul A.; Demetriou, Skevi
2009-01-01
Uncertainty may be an important component of the motivation provided by learning games, especially when associated with gaming rather than learning. Three studies are reported that explore the influence of gaming uncertainty on engagement with computer-based learning games. In the first study, children (10-11 years) played a simple maths quiz.…
M. Kasemann
Overview In autumn the main focus was to process and handle CRAFT data and to perform the Summer08 MC production. The operational aspects were well covered by regular Computing Shifts, experts on duty and Computing Run Coordination. At the Computing Resource Board (CRB) in October a model to account for service work at Tier 2s was approved. The computing resources for 2009 were reviewed for presentation at the C-RRB. The quarterly resource monitoring is continuing. Facilities/Infrastructure operations Operations during CRAFT data taking ran fine. This proved to be a very valuable experience for T0 workflows and operations. The transfers of custodial data to most T1s went smoothly. A first round of reprocessing started at the Tier-1 centers end of November; it will take about two weeks. The Computing Shifts procedure was tested full scale during this period and proved to be very efficient: 30 Computing Shifts Persons (CSP) and 10 Computing Resources Coordinators (CRC). The shift program for the shut down w...
Verification of uncertainty budgets
DEFF Research Database (Denmark)
Heydorn, Kaj; Madsen, B.S.
2005-01-01
The quality of analytical results is expressed by their uncertainty, as it is estimated on the basis of an uncertainty budget; little effort is, however, often spent on ascertaining the quality of the uncertainty budget. The uncertainty budget is based on circumstantial or historical data, and th...
M. Kasemann
Overview During the past three months activities were focused on data operations, testing and re-enforcing shift and operational procedures for data production and transfer, MC production and on user support. Planning of the computing resources in view of the new LHC calendar in ongoing. Two new task forces were created for supporting the integration work: Site Commissioning, which develops tools helping distributed sites to monitor job and data workflows, and Analysis Support, collecting the user experience and feedback during analysis activities and developing tools to increase efficiency. The development plan for DMWM for 2009/2011 was developed at the beginning of the year, based on the requirements from the Physics, Computing and Offline groups (see Offline section). The Computing management meeting at FermiLab on February 19th and 20th was an excellent opportunity discussing the impact and for addressing issues and solutions to the main challenges facing CMS computing. The lack of manpower is particul...
I. Fisk
2011-01-01
Introduction CMS distributed computing system performed well during the 2011 start-up. The events in 2011 have more pile-up and are more complex than last year; this results in longer reconstruction times and harder events to simulate. Significant increases in computing capacity were delivered in April for all computing tiers, and the utilisation and load is close to the planning predictions. All computing centre tiers performed their expected functionalities. Heavy-Ion Programme The CMS Heavy-Ion Programme had a very strong showing at the Quark Matter conference. A large number of analyses were shown. The dedicated heavy-ion reconstruction facility at the Vanderbilt Tier-2 is still involved in some commissioning activities, but is available for processing and analysis. Facilities and Infrastructure Operations Facility and Infrastructure operations have been active with operations and several important deployment tasks. Facilities participated in the testing and deployment of WMAgent and WorkQueue+Request...
P. McBride
The Computing Project is preparing for a busy year where the primary emphasis of the project moves towards steady operations. Following the very successful completion of Computing Software and Analysis challenge, CSA06, last fall, we have reorganized and established four groups in computing area: Commissioning, User Support, Facility/Infrastructure Operations and Data Operations. These groups work closely together with groups from the Offline Project in planning for data processing and operations. Monte Carlo production has continued since CSA06, with about 30M events produced each month to be used for HLT studies and physics validation. Monte Carlo production will continue throughout the year in the preparation of large samples for physics and detector studies ramping to 50 M events/month for CSA07. Commissioning of the full CMS computing system is a major goal for 2007. Site monitoring is an important commissioning component and work is ongoing to devise CMS specific tests to be included in Service Availa...
Bolève, A.; Vandemeulebrouck, J.; Grangeon, J.
2012-11-01
In the present study, we propose the combination of two geophysical techniques, which we have applied to a dyke located in southeastern France that has a visible downstream flood area: the self-potential (SP) and hydro-acoustic methods. These methods are sensitive to two different types of signals: electric signals and water-soil pressure disturbances, respectively. The advantages of the SP technique lie in the high rate of data acquisition, which allows assessment of long dykes, and direct diagnosis in terms of leakage area delimitation and quantification. Coupled with punctual hydro-acoustic cartography, a leakage position can be precisely located, therefore allowing specific remediation decisions with regard to the results of the geophysical investigation. Here, the precise localization of leakage from an earth dyke has been identified using SP and hydro-acoustic signals, with the permeability of the preferential fluid flow area estimated by forward SP modeling. Moreover, we propose a general 'abacus' diagram for the estimation of hydraulic permeability of dyke leakage according to the magnitude of over water SP anomalies and the associated uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Huerta, Gabriel [Univ. of New Mexico, Albuquerque, NM (United States)
2016-05-10
The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.
I. Fisk
2013-01-01
Computing activity had ramped down after the completion of the reprocessing of the 2012 data and parked data, but is increasing with new simulation samples for analysis and upgrade studies. Much of the Computing effort is currently involved in activities to improve the computing system in preparation for 2015. Operations Office Since the beginning of 2013, the Computing Operations team successfully re-processed the 2012 data in record time, not only by using opportunistic resources like the San Diego Supercomputer Center which was accessible, to re-process the primary datasets HTMHT and MultiJet in Run2012D much earlier than planned. The Heavy-Ion data-taking period was successfully concluded in February collecting almost 500 T. Figure 3: Number of events per month (data) In LS1, our emphasis is to increase efficiency and flexibility of the infrastructure and operation. Computing Operations is working on separating disk and tape at the Tier-1 sites and the full implementation of the xrootd federation ...
Atkinson, Paul
2011-01-01
The pixelated rectangle we spend most of our day staring at in silence is not the television as many long feared, but the computer-the ubiquitous portal of work and personal lives. At this point, the computer is almost so common we don't notice it in our view. It's difficult to envision that not that long ago it was a gigantic, room-sized structure only to be accessed by a few inspiring as much awe and respect as fear and mystery. Now that the machine has decreased in size and increased in popular use, the computer has become a prosaic appliance, little-more noted than a toaster. These dramati
Uncertainty quantification theory, implementation, and applications
Smith, Ralph C
2014-01-01
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers ca...
Experimental uncertainty estimation and statistics for data having interval uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)
2007-05-01
This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.
I. Fisk
2010-01-01
Introduction It has been a very active quarter in Computing with interesting progress in all areas. The activity level at the computing facilities, driven by both organised processing from data operations and user analysis, has been steadily increasing. The large-scale production of simulated events that has been progressing throughout the fall is wrapping-up and reprocessing with pile-up will continue. A large reprocessing of all the proton-proton data has just been released and another will follow shortly. The number of analysis jobs by users each day, that was already hitting the computing model expectations at the time of ICHEP, is now 33% higher. We are expecting a busy holiday break to ensure samples are ready in time for the winter conferences. Heavy Ion An activity that is still in progress is computing for the heavy-ion program. The heavy-ion events are collected without zero suppression, so the event size is much large at roughly 11 MB per event of RAW. The central collisions are more complex and...
M. Kasemann P. McBride Edited by M-C. Sawley with contributions from: P. Kreuzer D. Bonacorsi S. Belforte F. Wuerthwein L. Bauerdick K. Lassila-Perini M-C. Sawley
Introduction More than seventy CMS collaborators attended the Computing and Offline Workshop in San Diego, California, April 20-24th to discuss the state of readiness of software and computing for collisions. Focus and priority were given to preparations for data taking and providing room for ample dialog between groups involved in Commissioning, Data Operations, Analysis and MC Production. Throughout the workshop, aspects of software, operating procedures and issues addressing all parts of the computing model were discussed. Plans for the CMS participation in STEP’09, the combined scale testing for all four experiments due in June 2009, were refined. The article in CMS Times by Frank Wuerthwein gave a good recap of the highly collaborative atmosphere of the workshop. Many thanks to UCSD and to the organizers for taking care of this workshop, which resulted in a long list of action items and was definitely a success. A considerable amount of effort and care is invested in the estimate of the comput...
I. Fisk
2011-01-01
Introduction It has been a very active quarter in Computing with interesting progress in all areas. The activity level at the computing facilities, driven by both organised processing from data operations and user analysis, has been steadily increasing. The large-scale production of simulated events that has been progressing throughout the fall is wrapping-up and reprocessing with pile-up will continue. A large reprocessing of all the proton-proton data has just been released and another will follow shortly. The number of analysis jobs by users each day, that was already hitting the computing model expectations at the time of ICHEP, is now 33% higher. We are expecting a busy holiday break to ensure samples are ready in time for the winter conferences. Heavy Ion The Tier 0 infrastructure was able to repack and promptly reconstruct heavy-ion collision data. Two copies were made of the data at CERN using a large CASTOR disk pool, and the core physics sample was replicated ...
M. Kasemann
Introduction More than seventy CMS collaborators attended the Computing and Offline Workshop in San Diego, California, April 20-24th to discuss the state of readiness of software and computing for collisions. Focus and priority were given to preparations for data taking and providing room for ample dialog between groups involved in Commissioning, Data Operations, Analysis and MC Production. Throughout the workshop, aspects of software, operating procedures and issues addressing all parts of the computing model were discussed. Plans for the CMS participation in STEP’09, the combined scale testing for all four experiments due in June 2009, were refined. The article in CMS Times by Frank Wuerthwein gave a good recap of the highly collaborative atmosphere of the workshop. Many thanks to UCSD and to the organizers for taking care of this workshop, which resulted in a long list of action items and was definitely a success. A considerable amount of effort and care is invested in the estimate of the co...
I. Fisk
2012-01-01
Introduction Computing continued with a high level of activity over the winter in preparation for conferences and the start of the 2012 run. 2012 brings new challenges with a new energy, more complex events, and the need to make the best use of the available time before the Long Shutdown. We expect to be resource constrained on all tiers of the computing system in 2012 and are working to ensure the high-priority goals of CMS are not impacted. Heavy ions After a successful 2011 heavy-ion run, the programme is moving to analysis. During the run, the CAF resources were well used for prompt analysis. Since then in 2012 on average 200 job slots have been used continuously at Vanderbilt for analysis workflows. Operations Office As of 2012, the Computing Project emphasis has moved from commissioning to operation of the various systems. This is reflected in the new organisation structure where the Facilities and Data Operations tasks have been merged into a common Operations Office, which now covers everything ...
M. Kasemann
Introduction During the past six months, Computing participated in the STEP09 exercise, had a major involvement in the October exercise and has been working with CMS sites on improving open issues relevant for data taking. At the same time operations for MC production, real data reconstruction and re-reconstructions and data transfers at large scales were performed. STEP09 was successfully conducted in June as a joint exercise with ATLAS and the other experiments. It gave good indication about the readiness of the WLCG infrastructure with the two major LHC experiments stressing the reading, writing and processing of physics data. The October Exercise, in contrast, was conducted as an all-CMS exercise, where Physics, Computing and Offline worked on a common plan to exercise all steps to efficiently access and analyze data. As one of the major results, the CMS Tier-2s demonstrated to be fully capable for performing data analysis. In recent weeks, efforts were devoted to CMS Computing readiness. All th...
P. McBride
It has been a very active year for the computing project with strong contributions from members of the global community. The project has focused on site preparation and Monte Carlo production. The operations group has begun processing data from P5 as part of the global data commissioning. Improvements in transfer rates and site availability have been seen as computing sites across the globe prepare for large scale production and analysis as part of CSA07. Preparations for the upcoming Computing Software and Analysis Challenge CSA07 are progressing. Ian Fisk and Neil Geddes have been appointed as coordinators for the challenge. CSA07 will include production tests of the Tier-0 production system, reprocessing at the Tier-1 sites and Monte Carlo production at the Tier-2 sites. At the same time there will be a large analysis exercise at the Tier-2 centres. Pre-production simulation of the Monte Carlo events for the challenge is beginning. Scale tests of the Tier-0 will begin in mid-July and the challenge it...
I. Fisk
2010-01-01
Introduction The first data taking period of November produced a first scientific paper, and this is a very satisfactory step for Computing. It also gave the invaluable opportunity to learn and debrief from this first, intense period, and make the necessary adaptations. The alarm procedures between different groups (DAQ, Physics, T0 processing, Alignment/calibration, T1 and T2 communications) have been reinforced. A major effort has also been invested into remodeling and optimizing operator tasks in all activities in Computing, in parallel with the recruitment of new Cat A operators. The teams are being completed and by mid year the new tasks will have been assigned. CRB (Computing Resource Board) The Board met twice since last CMS week. In December it reviewed the experience of the November data-taking period and could measure the positive improvements made for the site readiness. It also reviewed the policy under which Tier-2 are associated with Physics Groups. Such associations are decided twice per ye...
M. Kasemann
CCRC’08 challenges and CSA08 During the February campaign of the Common Computing readiness challenges (CCRC’08), the CMS computing team had achieved very good results. The link between the detector site and the Tier0 was tested by gradually increasing the number of parallel transfer streams well beyond the target. Tests covered the global robustness at the Tier0, processing a massive number of very large files and with a high writing speed to tapes. Other tests covered the links between the different Tiers of the distributed infrastructure and the pre-staging and reprocessing capacity of the Tier1’s: response time, data transfer rate and success rate for Tape to Buffer staging of files kept exclusively on Tape were measured. In all cases, coordination with the sites was efficient and no serious problem was found. These successful preparations prepared the ground for the second phase of the CCRC’08 campaign, in May. The Computing Software and Analysis challen...
Estimating uncertainties in complex joint inverse problems
Afonso, Juan Carlos
2016-04-01
Sources of uncertainty affecting geophysical inversions can be classified either as reflective (i.e. the practitioner is aware of her/his ignorance) or non-reflective (i.e. the practitioner does not know that she/he does not know!). Although we should be always conscious of the latter, the former are the ones that, in principle, can be estimated either empirically (by making measurements or collecting data) or subjectively (based on the experience of the researchers). For complex parameter estimation problems in geophysics, subjective estimation of uncertainty is the most common type. In this context, probabilistic (aka Bayesian) methods are commonly claimed to offer a natural and realistic platform from which to estimate model uncertainties. This is because in the Bayesian approach, errors (whatever their nature) can be naturally included as part of the global statistical model, the solution of which represents the actual solution to the inverse problem. However, although we agree that probabilistic inversion methods are the most powerful tool for uncertainty estimation, the common claim that they produce "realistic" or "representative" uncertainties is not always justified. Typically, ALL UNCERTAINTY ESTIMATES ARE MODEL DEPENDENT, and therefore, besides a thorough characterization of experimental uncertainties, particular care must be paid to the uncertainty arising from model errors and input uncertainties. We recall here two quotes by G. Box and M. Gunzburger, respectively, of special significance for inversion practitioners and for this session: "…all models are wrong, but some are useful" and "computational results are believed by no one, except the person who wrote the code". In this presentation I will discuss and present examples of some problems associated with the estimation and quantification of uncertainties in complex multi-observable probabilistic inversions, and how to address them. Although the emphasis will be on sources of uncertainty related
Uncertainty Propagation for Terrestrial Mobile Laser Scanner
Mezian, c.; Vallet, Bruno; Soheilian, Bahman; Paparoditis, Nicolas
2016-06-01
Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.
I. Fisk
2013-01-01
Computing operation has been lower as the Run 1 samples are completing and smaller samples for upgrades and preparations are ramping up. Much of the computing activity is focusing on preparations for Run 2 and improvements in data access and flexibility of using resources. Operations Office Data processing was slow in the second half of 2013 with only the legacy re-reconstruction pass of 2011 data being processed at the sites. Figure 1: MC production and processing was more in demand with a peak of over 750 Million GEN-SIM events in a single month. Figure 2: The transfer system worked reliably and efficiently and transferred on average close to 520 TB per week with peaks at close to 1.2 PB. Figure 3: The volume of data moved between CMS sites in the last six months The tape utilisation was a focus for the operation teams with frequent deletion campaigns from deprecated 7 TeV MC GEN-SIM samples to INVALID datasets, which could be cleaned up...
I. Fisk
2012-01-01
Introduction Computing activity has been running at a sustained, high rate as we collect data at high luminosity, process simulation, and begin to process the parked data. The system is functional, though a number of improvements are planned during LS1. Many of the changes will impact users, we hope only in positive ways. We are trying to improve the distributed analysis tools as well as the ability to access more data samples more transparently. Operations Office Figure 2: Number of events per month, for 2012 Since the June CMS Week, Computing Operations teams successfully completed data re-reconstruction passes and finished the CMSSW_53X MC campaign with over three billion events available in AOD format. Recorded data was successfully processed in parallel, exceeding 1.2 billion raw physics events per month for the first time in October 2012 due to the increase in data-parking rate. In parallel, large efforts were dedicated to WMAgent development and integrati...
Matthias Kasemann
Overview The main focus during the summer was to handle data coming from the detector and to perform Monte Carlo production. The lessons learned during the CCRC and CSA08 challenges in May were addressed by dedicated PADA campaigns lead by the Integration team. Big improvements were achieved in the stability and reliability of the CMS Tier1 and Tier2 centres by regular and systematic follow-up of faults and errors with the help of the Savannah bug tracking system. In preparation for data taking the roles of a Computing Run Coordinator and regular computing shifts monitoring the services and infrastructure as well as interfacing to the data operations tasks are being defined. The shift plan until the end of 2008 is being put together. User support worked on documentation and organized several training sessions. The ECoM task force delivered the report on “Use Cases for Start-up of pp Data-Taking” with recommendations and a set of tests to be performed for trigger rates much higher than the ...
M. Kasemann
Introduction A large fraction of the effort was focused during the last period into the preparation and monitoring of the February tests of Common VO Computing Readiness Challenge 08. CCRC08 is being run by the WLCG collaboration in two phases, between the centres and all experiments. The February test is dedicated to functionality tests, while the May challenge will consist of running at all centres and with full workflows. For this first period, a number of functionality checks of the computing power, data repositories and archives as well as network links are planned. This will help assess the reliability of the systems under a variety of loads, and identifying possible bottlenecks. Many tests are scheduled together with other VOs, allowing the full scale stress test. The data rates (writing, accessing and transfer¬ring) are being checked under a variety of loads and operating conditions, as well as the reliability and transfer rates of the links between Tier-0 and Tier-1s. In addition, the capa...
Contributions from I. Fisk
2012-01-01
Introduction The start of the 2012 run has been busy for Computing. We have reconstructed, archived, and served a larger sample of new data than in 2011, and we are in the process of producing an even larger new sample of simulations at 8 TeV. The running conditions and system performance are largely what was anticipated in the plan, thanks to the hard work and preparation of many people. Heavy ions Heavy Ions has been actively analysing data and preparing for conferences. Operations Office Figure 6: Transfers from all sites in the last 90 days For ICHEP and the Upgrade efforts, we needed to produce and process record amounts of MC samples while supporting the very successful data-taking. This was a large burden, especially on the team members. Nevertheless the last three months were very successful and the total output was phenomenal, thanks to our dedicated site admins who keep the sites operational and the computing project members who spend countless hours nursing the...
P. MacBride
The Computing Software and Analysis Challenge CSA07 has been the main focus of the Computing Project for the past few months. Activities began over the summer with the preparation of the Monte Carlo data sets for the challenge and tests of the new production system at the Tier-0 at CERN. The pre-challenge Monte Carlo production was done in several steps: physics generation, detector simulation, digitization, conversion to RAW format and the samples were run through the High Level Trigger (HLT). The data was then merged into three "Soups": Chowder (ALPGEN), Stew (Filtered Pythia) and Gumbo (Pythia). The challenge officially started when the first Chowder events were reconstructed on the Tier-0 on October 3rd. The data operations teams were very busy during the the challenge period. The MC production teams continued with signal production and processing while the Tier-0 and Tier-1 teams worked on splitting the Soups into Primary Data Sets (PDS), reconstruction and skimming. The storage sys...
2010-01-01
Introduction Just two months after the “LHC First Physics” event of 30th March, the analysis of the O(200) million 7 TeV collision events in CMS accumulated during the first 60 days is well under way. The consistency of the CMS computing model has been confirmed during these first weeks of data taking. This model is based on a hierarchy of use-cases deployed between the different tiers and, in particular, the distribution of RECO data to T1s, who then serve data on request to T2s, along a topology known as “fat tree”. Indeed, during this period this model was further extended by almost full “mesh” commissioning, meaning that RECO data were shipped to T2s whenever possible, enabling additional physics analyses compared with the “fat tree” model. Computing activities at the CMS Analysis Facility (CAF) have been marked by a good time response for a load almost evenly shared between ALCA (Alignment and Calibration tasks - highest p...
不确定感知的自适应云计算服务组合%Uncertainty-Aware Adaptive Service Composition in Cloud Computing
Institute of Scientific and Technical Information of China (English)
任丽芳; 王文剑; 许行
2016-01-01
云计算服务组合是从众多分布在不同云计算平台上的远程服务中选择合适的组件服务来构建可伸缩的松耦合的增值应用．传统的服务组合方法通常将服务选择与服务组合分阶段进行，由于云计算环境的动态性和服务自身演化的随机性，不能保证选择阶段性能最优的服务在组合服务执行阶段依然是最优的．考虑到云计算环境服务组合的动态性和随机性，建立基于部分可观测 M arkov 决策过程（partially observable Markov decision process ， POMDP ）的服务组合模型 SC＿POMDP （service composition based on POMDP ），并设计用于模型求解的Q 学习算法．SC＿POMDP模型在组合服务运行中动态地进行服务质量（quality of service ，QoS）最优的组件服务选择，且认为组合服务运行的环境状态是不确定的，同时SC＿POM DP考虑了组件服务间的兼容性，可保证服务组合对实际情境的适应性．仿真实验表明，所提出的方法能成功地解决不同规模的服务组合问题，在出现不同比率的服务失效时，SC＿POMDP仍然能动态地选择可用的最优组件服务，保证服务组合能成功地执行．与已有方法相比，SC＿POMDP方法所选的服务有更优的响应时间和吞吐量，表明SC＿POMDP可有效地提高服务组合的自适应性．%Cloud computing service composition is to select appropriate component services from numerous of services distributed in different clouds to build scalable loose coupling value‐added applications .Traditional service composition methods are usually divided into selection stage and composition stage .Hardly guaranteeing the services with the best performance in the selection stage are still optimal in the execution stage because of the dynamic nature of the cloud computing environment and the stochastic nature of services evolution .Focusing on these two natures of service composition in cloud
I. Fisk
2011-01-01
Introduction The Computing Team successfully completed the storage, initial processing, and distribution for analysis of proton-proton data in 2011. There are still a variety of activities ongoing to support winter conference activities and preparations for 2012. Heavy ions The heavy-ion run for 2011 started in early November and has already demonstrated good machine performance and success of some of the more advanced workflows planned for 2011. Data collection will continue until early December. Facilities and Infrastructure Operations Operational and deployment support for WMAgent and WorkQueue+Request Manager components, routinely used in production by Data Operations, are provided. The GlideInWMS and components installation are now deployed at CERN, which is added to the GlideInWMS factory placed in the US. There has been new operational collaboration between the CERN team and the UCSD GlideIn factory operators, covering each others time zones by monitoring/debugging pilot jobs sent from the facto...
Uncertainty Quantification in Climate Modeling
Sargsyan, K.; Safta, C.; Berry, R.; Debusschere, B.; Najm, H.
2011-12-01
We address challenges that sensitivity analysis and uncertainty quantification methods face when dealing with complex computational models. In particular, climate models are computationally expensive and typically depend on a large number of input parameters. We consider the Community Land Model (CLM), which consists of a nested computational grid hierarchy designed to represent the spatial heterogeneity of the land surface. Each computational cell can be composed of multiple land types, and each land type can incorporate one or more sub-models describing the spatial and depth variability. Even for simulations at a regional scale, the computational cost of a single run is quite high and the number of parameters that control the model behavior is very large. Therefore, the parameter sensitivity analysis and uncertainty propagation face significant difficulties for climate models. This work employs several algorithmic avenues to address some of the challenges encountered by classical uncertainty quantification methodologies when dealing with expensive computational models, specifically focusing on the CLM as a primary application. First of all, since the available climate model predictions are extremely sparse due to the high computational cost of model runs, we adopt a Bayesian framework that effectively incorporates this lack-of-knowledge as a source of uncertainty, and produces robust predictions with quantified uncertainty even if the model runs are extremely sparse. In particular, we infer Polynomial Chaos spectral expansions that effectively encode the uncertain input-output relationship and allow efficient propagation of all sources of input uncertainties to outputs of interest. Secondly, the predictability analysis of climate models strongly suffers from the curse of dimensionality, i.e. the large number of input parameters. While single-parameter perturbation studies can be efficiently performed in a parallel fashion, the multivariate uncertainty analysis
M. Kasemann
CMS relies on a well functioning, distributed computing infrastructure. The Site Availability Monitoring (SAM) and the Job Robot submission have been very instrumental for site commissioning in order to increase availability of more sites such that they are available to participate in CSA07 and are ready to be used for analysis. The commissioning process has been further developed, including "lessons learned" documentation via the CMS twiki. Recently the visualization, presentation and summarizing of SAM tests for sites has been redesigned, it is now developed by the central ARDA project of WLCG. Work to test the new gLite Workload Management System was performed; a 4 times increase in throughput with respect to LCG Resource Broker is observed. CMS has designed and launched a new-generation traffic load generator called "LoadTest" to commission and to keep exercised all data transfer routes in the CMS PhE-DEx topology. Since mid-February, a transfer volume of about 12 P...
Uncertainties in Safety Analysis. A literature review
Energy Technology Data Exchange (ETDEWEB)
Ekberg, C. [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Nuclear Chemistry
1995-05-01
The purpose of the presented work has been to give a short summary of the origins of many uncertainties arising in the designing and performance assessment of a repository for spent nuclear fuel. Some different methods to treat these uncertainties is also included. The methods and conclusions are in many cases general in the sense that they are applicable to many other disciplines where simulations are used. As a conclusion it may be noted that uncertainties of different origin have been discussed and debated, but one large group, e.g. computer simulations, where the methods to make a more explicit investigation exists, have not been investigated in a satisfying way. 50 refs.
Eaton, Eric; University of Pennsylvania; Gomes, Carla P.; Cornell University; Williams, Brian; Massachusetts Institute of Technology
2014-01-01
Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial provides an overview of artificial intelligence for computational sustainability, and introduces this special issue of AI Magazine.
Heisenberg's uncertainty principle
Busch, Paul; Heinonen, Teiko; Lahti, Pekka
2007-01-01
Heisenberg's uncertainty principle is usually taken to express a limitation of operational possibilities imposed by quantum mechanics. Here we demonstrate that the full content of this principle also includes its positive role as a condition ensuring that mutually exclusive experimental options can be reconciled if an appropriate trade-off is accepted. The uncertainty principle is shown to appear in three manifestations, in the form of uncertainty relations: for the widths of the position and...
Commonplaces and social uncertainty
DEFF Research Database (Denmark)
Lassen, Inger
2008-01-01
This article explores the concept of uncertainty in four focus group discussions about genetically modified food. In the discussions, members of the general public interact with food biotechnology scientists while negotiating their attitudes towards genetic engineering. Their discussions offer...... risk discourse (Myers 2005; 2007). In additional, however, I argue that commonplaces are used to mitigate feelings of insecurity caused by uncertainty and to negotiate new codes of moral conduct. Keywords: uncertainty, commonplaces, risk discourse, focus groups, appraisal...
[Ethics, empiricism and uncertainty].
Porz, R; Zimmermann, H; Exadaktylos, A K
2011-01-01
Accidents can lead to difficult boundary situations. Such situations often take place in the emergency units. The medical team thus often and inevitably faces professional uncertainty in their decision-making. It is essential to communicate these uncertainties within the medical team, instead of downplaying or overriding existential hurdles in decision-making. Acknowledging uncertainties might lead to alert and prudent decisions. Thus uncertainty can have ethical value in treatment or withdrawal of treatment. It does not need to be covered in evidence-based arguments, especially as some singular situations of individual tragedies cannot be grasped in terms of evidence-based medicine.
Uncertainty in artificial intelligence
Kanal, LN
1986-01-01
How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Understanding Theoretical Uncertainties in Perturbative QCD Computations
DEFF Research Database (Denmark)
Jenniches, Laura Katharina
effective field theories and perturbative QCD to predict the effect of New Physics on measurements at the LHC and at other future colliders. We use heavy-quark, heavy-scalar and soft-collinear effective theory to calculate a three-body cascade decay at NLO QCD in the expansion-by-regions formalism...... discuss an extension of the Cacciari-Houdeau approach to observables with hadrons in the initial state....
Predictive uncertainty in auditory sequence processing.
Hansen, Niels Chr; Pearce, Marcus T
2014-01-01
Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty-a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.
Uncertainty in flood risk mapping
Gonçalves, Luisa M. S.; Fonte, Cidália C.; Gomes, Ricardo
2014-05-01
A flood refers to a sharp increase of water level or volume in rivers and seas caused by sudden rainstorms or melting ice due to natural factors. In this paper, the flooding of riverside urban areas caused by sudden rainstorms will be studied. In this context, flooding occurs when the water runs above the level of the minor river bed and enters the major river bed. The level of the major bed determines the magnitude and risk of the flooding. The prediction of the flooding extent is usually deterministic, and corresponds to the expected limit of the flooded area. However, there are many sources of uncertainty in the process of obtaining these limits, which influence the obtained flood maps used for watershed management or as instruments for territorial and emergency planning. In addition, small variations in the delineation of the flooded area can be translated into erroneous risk prediction. Therefore, maps that reflect the uncertainty associated with the flood modeling process have started to be developed, associating a degree of likelihood with the boundaries of the flooded areas. In this paper an approach is presented that enables the influence of the parameters uncertainty to be evaluated, dependent on the type of Land Cover Map (LCM) and Digital Elevation Model (DEM), on the estimated values of the peak flow and the delineation of flooded areas (different peak flows correspond to different flood areas). The approach requires modeling the DEM uncertainty and its propagation to the catchment delineation. The results obtained in this step enable a catchment with fuzzy geographical extent to be generated, where a degree of possibility of belonging to the basin is assigned to each elementary spatial unit. Since the fuzzy basin may be considered as a fuzzy set, the fuzzy area of the basin may be computed, generating a fuzzy number. The catchment peak flow is then evaluated using fuzzy arithmetic. With this methodology a fuzzy number is obtained for the peak flow
Van Nooyen, R.R.P.; Hrachowitz, M.; Kolechkina, A.G.
2014-01-01
Even without uncertainty about the model structure or parameters, the output of a hydrological model run still contains several sources of uncertainty. These are: measurement errors affecting the input, the transition from continuous time and space to discrete time and space, which causes loss of in
Capel, H.W.; Cramer, J.S.; Estevez-Uscanga, O.
1995-01-01
'Uncertainty and chance' is a subject with a broad span, in that there is no academic discipline or walk of life that is not beset by uncertainty and chance. In this book a range of approaches is represented by authors from varied disciplines: natural sciences, mathematics, social sciences and medic
Measurement uncertainty and probability
Willink, Robin
2013-01-01
A measurement result is incomplete without a statement of its 'uncertainty' or 'margin of error'. But what does this statement actually tell us? By examining the practical meaning of probability, this book discusses what is meant by a '95 percent interval of measurement uncertainty', and how such an interval can be calculated. The book argues that the concept of an unknown 'target value' is essential if probability is to be used as a tool for evaluating measurement uncertainty. It uses statistical concepts, such as a conditional confidence interval, to present 'extended' classical methods for evaluating measurement uncertainty. The use of the Monte Carlo principle for the simulation of experiments is described. Useful for researchers and graduate students, the book also discusses other philosophies relating to the evaluation of measurement uncertainty. It employs clear notation and language to avoid the confusion that exists in this controversial field of science.
Economic uncertainty and econophysics
Schinckus, Christophe
2009-10-01
The objective of this paper is to provide a methodological link between econophysics and economics. I will study a key notion of both fields: uncertainty and the ways of thinking about it developed by the two disciplines. After having presented the main economic theories of uncertainty (provided by Knight, Keynes and Hayek), I show how this notion is paradoxically excluded from the economic field. In economics, uncertainty is totally reduced by an a priori Gaussian framework-in contrast to econophysics, which does not use a priori models because it works directly on data. Uncertainty is then not shaped by a specific model, and is partially and temporally reduced as models improve. This way of thinking about uncertainty has echoes in the economic literature. By presenting econophysics as a Knightian method, and a complementary approach to a Hayekian framework, this paper shows that econophysics can be methodologically justified from an economic point of view.
Physical Uncertainty Bounds (PUB)
Energy Technology Data Exchange (ETDEWEB)
Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-03-19
This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.
2007-01-01
Predicting the performance of large scale plants can be difficult due to model uncertainties etc, meaning that one can be almost certain that the prediction will diverge from the plant performance with time. In this paper output multiplicative uncertainty models are used as dynamical models of th...... models, is applied to two different sets of measured plant data. The computed uncertainty bounds cover the measured plant output, while the nominal prediction is outside these uncertainty bounds for some samples in these examples. ......Predicting the performance of large scale plants can be difficult due to model uncertainties etc, meaning that one can be almost certain that the prediction will diverge from the plant performance with time. In this paper output multiplicative uncertainty models are used as dynamical models...... of the prediction error. These proposed dynamical uncertainty models result in an upper and lower bound on the predicted performance of the plant. The dynamical uncertainty models are used to estimate the uncertainty of the predicted performance of a coal-fired power plant. The proposed scheme, which uses dynamical...
Quantum preparation uncertainty and lack of information
Rozpędek, Filip; Kaniewski, Jędrzej; Coles, Patrick J.; Wehner, Stephanie
2017-02-01
The quantum uncertainty principle famously predicts that there exist measurements that are inherently incompatible, in the sense that their outcomes cannot be predicted simultaneously. In contrast, no such uncertainty exists in the classical domain, where all uncertainty results from ignorance about the exact state of the physical system. Here, we critically examine the concept of preparation uncertainty and ask whether similarly in the quantum regime, some of the uncertainty that we observe can actually also be understood as a lack of information (LOI), albeit a lack of quantum information. We answer this question affirmatively by showing that for the well known measurements employed in BB84 quantum key distribution (Bennett and Brassard 1984 Int. Conf. on Computer System and Signal Processing), the amount of uncertainty can indeed be related to the amount of available information about additional registers determining the choice of the measurement. We proceed to show that also for other measurements the amount of uncertainty is in part connected to a LOI. Finally, we discuss the conceptual implications of our observation to the security of cryptographic protocols that make use of BB84 states.
Optimal Universal Uncertainty Relations
Li, Tao; Xiao, Yunlong; Ma, Teng; Fei, Shao-Ming; Jing, Naihuan; Li-Jost, Xianqing; Wang, Zhi-Xi
2016-01-01
We study universal uncertainty relations and present a method called joint probability distribution diagram to improve the majorization bounds constructed independently in [Phys. Rev. Lett. 111, 230401 (2013)] and [J. Phys. A. 46, 272002 (2013)]. The results give rise to state independent uncertainty relations satisfied by any nonnegative Schur-concave functions. On the other hand, a remarkable recent result of entropic uncertainty relation is the direct-sum majorization relation. In this paper, we illustrate our bounds by showing how they provide a complement to that in [Phys. Rev. A. 89, 052115 (2014)]. PMID:27775010
Quantum computing and probability.
Ferry, David K
2009-11-25
Over the past two decades, quantum computing has become a popular and promising approach to trying to solve computationally difficult problems. Missing in many descriptions of quantum computing is just how probability enters into the process. Here, we discuss some simple examples of how uncertainty and probability enter, and how this and the ideas of quantum computing challenge our interpretations of quantum mechanics. It is found that this uncertainty can lead to intrinsic decoherence, and this raises challenges for error correction.
Menger, Fredric M
2010-09-01
It might come as a disappointment to some chemists, but just as there are uncertainties in physics and mathematics, there are some chemistry questions we may never know the answer to either, suggests Fredric M. Menger.
Uncertainty, rationality, and agency
Hoek, Wiebe van der
2006-01-01
Goes across 'classical' borderlines of disciplinesUnifies logic, game theory, and epistemics and studies them in an agent-settingCombines classical and novel approaches to uncertainty, rationality, and agency
Lemaire, Maurice
2014-01-01
Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.
Generalized uncertainty principles
Machluf, Ronny
2008-01-01
The phenomenon in the essence of classical uncertainty principles is well known since the thirties of the last century. We introduce a new phenomenon which is in the essence of a new notion that we introduce: "Generalized Uncertainty Principles". We show the relation between classical uncertainty principles and generalized uncertainty principles. We generalized "Landau-Pollak-Slepian" uncertainty principle. Our generalization relates the following two quantities and two scaling parameters: 1) The weighted time spreading $\\int_{-\\infty}^\\infty |f(x)|^2w_1(x)dx$, ($w_1(x)$ is a non-negative function). 2) The weighted frequency spreading $\\int_{-\\infty}^\\infty |\\hat{f}(\\omega)|^2w_2(\\omega)d\\omega$. 3) The time weight scale $a$, ${w_1}_a(x)=w_1(xa^{-1})$ and 4) The frequency weight scale $b$, ${w_2}_b(\\omega)=w_2(\\omega b^{-1})$. "Generalized Uncertainty Principle" is an inequality that summarizes the constraints on the relations between the two spreading quantities and two scaling parameters. For any two reason...
The uncertainties of magnetic properties measurements of electrical sheet steel
Ahlers, H
2000-01-01
In this work, uncertainties in measurements of magnetic properties of Epstein- and single-sheet samples have been determined according to the 'Guide To The Expression Of Uncertainty In Measurement', [International Organization for Standardization (1993)]. They were calculated for the results at predicted values of parameters taking into account the non-linear dependences. The measurement results and the uncertainties are calculated simultaneously by a computer program.
Uncertainty Estimates for Theoretical Atomic and Molecular Data
Chung, H -K; Bartschat, K; Csaszar, A G; Drake, G W F; Kirchner, T; Kokoouline, V; Tennyson, J
2016-01-01
Sources of uncertainty are reviewed for calculated atomic and molecular data that are important for plasma modeling: atomic and molecular structure and cross sections for electron-atom, electron-molecule, and heavy particle collisions. We concentrate on model uncertainties due to approximations to the fundamental many-body quantum mechanical equations and we aim to provide guidelines to estimate uncertainties as a routine part of computations of data for structure and scattering.
Estimating the measurement uncertainty in forensic blood alcohol analysis.
Gullberg, Rod G
2012-04-01
For many reasons, forensic toxicologists are being asked to determine and report their measurement uncertainty in blood alcohol analysis. While understood conceptually, the elements and computations involved in determining measurement uncertainty are generally foreign to most forensic toxicologists. Several established and well-documented methods are available to determine and report the uncertainty in blood alcohol measurement. A straightforward bottom-up approach is presented that includes: (1) specifying the measurand, (2) identifying the major components of uncertainty, (3) quantifying the components, (4) statistically combining the components and (5) reporting the results. A hypothetical example is presented that employs reasonable estimates for forensic blood alcohol analysis assuming headspace gas chromatography. These computations are easily employed in spreadsheet programs as well. Determining and reporting measurement uncertainty is an important element in establishing fitness-for-purpose. Indeed, the demand for such computations and information from the forensic toxicologist will continue to increase.
Network planning under uncertainties
Ho, Kwok Shing; Cheung, Kwok Wai
2008-11-01
One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a
Market uncertainty; Markedsusikkerhet
Energy Technology Data Exchange (ETDEWEB)
Doorman, Gerard; Holtan, Jon Anders; Mo, Birger; Groenli, Helle; Haaland, Magnar; Grinden, Bjoern
1997-04-10
In Norway, the project ``Market uncertainty`` has been in progress for over two years and resulted in increased skill in the use of the Grid System Operation Model. This report classifies some of the factors which lead to uncertainties in the electric power market. It has been examined whether these factors should be, or can be, modelled in the available simulation models. Some of the factors have been further considered and methods of modelling the associated uncertainties have been examined. It is concluded that (1) There is a need for automatic simulation of several scenarios in the model, and these scenarios should incorporate probability parameters, (2) At first it is most important that one can handle uncertainties in fuel prices and demand, (3) Market uncertainty which is due to irrational behaviour should be dealt with in a separate model. The difference between real and simulated prices should be analysed and modelled with a time series model, (4) Risk should be included in the Vansimtap model by way of feedback from simulations, (5) The marginal values of stored water as calculated by means of the various methods in use should be compared systematically. 9 refs., 16 figs., 5 tabs.
Interpreting uncertainty terms.
Holtgraves, Thomas
2014-08-01
Uncertainty terms (e.g., some, possible, good, etc.) are words that do not have a fixed referent and hence are relatively ambiguous. A model is proposed that specifies how, from the hearer's perspective, recognition of facework as a potential motive for the use of an uncertainty term results in a calibration of the intended meaning of that term. Four experiments are reported that examine the impact of face threat, and the variables that affect it (e.g., power), on the manner in which a variety of uncertainty terms (probability terms, quantifiers, frequency terms, etc.) are interpreted. Overall, the results demonstrate that increased face threat in a situation will result in a more negative interpretation of an utterance containing an uncertainty term. That the interpretation of so many different types of uncertainty terms is affected in the same way suggests the operation of a fundamental principle of language use, one with important implications for the communication of risk, subjective experience, and so on.
About uncertainties in practical salinity calculations
Directory of Open Access Journals (Sweden)
M. Le Menn
2009-10-01
Full Text Available Salinity is a quantity computed, in the actual state of the art, from conductivity ratio measurements, knowing temperature and pressure at the time of the measurement and using the Practical Salinity Scale algorithm of 1978 (PSS-78 which gives practical salinity values S. The uncertainty expected on PSS-78 values is ±0.002, but nothing has ever been detailed about the method to work out this uncertainty, and the sources of errors to include in this calculation. Following a guide edited by the Bureau International des Poids et Mesures (BIPM, this paper assess, by two independent methods, the uncertainties of salinity values obtained from a laboratory salinometer and Conductivity-Temperature-Depth (CTD measurements after laboratory calibration of a conductivity cell. The results show that the part due to the PSS-78 relations fits is sometimes as much significant as the instruments one's. This is particularly the case with CTD measurements where correlations between the variables contribute to decrease largely the uncertainty on S, even when the expanded uncertainties on conductivity cells calibrations are largely up of 0.002 mS/cm. The relations given in this publication, and obtained with the normalized GUM method, allow a real analysis of the uncertainties sources and they can be used in a more general way, with instruments having different specifications.
Directory of Open Access Journals (Sweden)
Francisco Cutanda Henríquez
2011-01-01
computation and experimental uncertainty. This work utilizes mathematical methods to analyse comparisons, so that uncertainty can be taken into account. Therefore, false rejections due to uncertainty do not take place and there is no need to expand tolerances to take uncertainty into account. The methods provided are based on the rules of uncertainty propagation and help obtain rigorous pass/fail criteria, based on experimental information.
Nuclear Data Uncertainties in 2004: A Perspective
Smith, Donald L.
2005-05-01
Interest in nuclear data uncertainties is growing robustly after having languished for several years. Renewed attention to this topic is being motivated by the practical need for assuring that nuclear systems will be safe, reliable, and cost effective, according to the individual requirements of each specific nuclear technology. Furthermore, applications are emerging in certain areas of basic nuclear science, e.g., in astrophysics, where, until recently, attention has focused mainly on understanding basic concepts and physics principles rather than on dealing with detailed quantitative information. The availability of fast computers and the concurrent development of sophisticated software enable nuclear data uncertainty information to be used more effectively than ever before. For example, data uncertainties and associated methodologies play useful roles in advanced data measurement, analysis, and evaluation procedures. Unfortunately, the current inventory of requisite uncertainty information is rather limited when measured against these evolving demands. Consequently, there is a real need to generate more comprehensive and reasonable nuclear data uncertainty information, and to make this available relatively soon in suitable form for use in the computer codes employed for nuclear analyses and the development of advanced nuclear energy systems. This conference contribution discusses several conceptual and technical issues that need to be addressed in meeting this demand during the next few years. The role of data uncertainties in several areas of nuclear science will also be mentioned briefly. Finally, the opportunities that ultimately will be afforded by the availability of more extensive and reasonable uncertainty information, and some technical challenges to master, will also be explored in this paper.
Measurement uncertainty relations
Energy Technology Data Exchange (ETDEWEB)
Busch, Paul, E-mail: paul.busch@york.ac.uk [Department of Mathematics, University of York, York (United Kingdom); Lahti, Pekka, E-mail: pekka.lahti@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Werner, Reinhard F., E-mail: reinhard.werner@itp.uni-hannover.de [Institut für Theoretische Physik, Leibniz Universität, Hannover (Germany)
2014-04-15
Measurement uncertainty relations are quantitative bounds on the errors in an approximate joint measurement of two observables. They can be seen as a generalization of the error/disturbance tradeoff first discussed heuristically by Heisenberg. Here we prove such relations for the case of two canonically conjugate observables like position and momentum, and establish a close connection with the more familiar preparation uncertainty relations constraining the sharpness of the distributions of the two observables in the same state. Both sets of relations are generalized to means of order α rather than the usual quadratic means, and we show that the optimal constants are the same for preparation and for measurement uncertainty. The constants are determined numerically and compared with some bounds in the literature. In both cases, the near-saturation of the inequalities entails that the state (resp. observable) is uniformly close to a minimizing one.
SAGD optimization under uncertainty
Energy Technology Data Exchange (ETDEWEB)
Gossuin, J.; Naccache, P. [Schlumberger SIS, Abingdon (United Kingdom); Bailley, W.; Couet, B. [Schlumberger-Doll Research, Cambridge, MA, (United States)
2011-07-01
In the heavy oil industry, the steam assisted gravity drainage process is often used to enhance oil recovery but this is a costly method and ways to make it more efficient are needed. Multiple methods have been developed to optimize the SAGD process but none of them explicitly considered uncertainty. This paper presents an optimization method in the presence of reservoir uncertainty. This process was tested on an SAGD model where three equi-probable geological models are possible. Preparatory steps were first performed to identify key variables and the optimization model was then proposed. The method was shown to be successful in handling a significant number of uncertainties, optimizing the SAGD process and preventing premature steam channels that can choke production. The optimization method presented herein was successfully applied to an SAGD process and was shown to provide better strategies than sensitivity analysis while handling more complex problems.
Uncertainty in artificial intelligence
Levitt, TS; Lemmer, JF; Shachter, RD
1990-01-01
Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally i
Quantification and Propagation of Nuclear Data Uncertainties
Rising, Michael E.
The use of several uncertainty quantification and propagation methodologies is investigated in the context of the prompt fission neutron spectrum (PFNS) uncertainties and its impact on critical reactor assemblies. First, the first-order, linear Kalman filter is used as a nuclear data evaluation and uncertainty quantification tool combining available PFNS experimental data and a modified version of the Los Alamos (LA) model. The experimental covariance matrices, not generally given in the EXFOR database, are computed using the GMA methodology used by the IAEA to establish more appropriate correlations within each experiment. Then, using systematics relating the LA model parameters across a suite of isotopes, the PFNS for both the uranium and plutonium actinides are evaluated leading to a new evaluation including cross-isotope correlations. Next, an alternative evaluation approach, the unified Monte Carlo (UMC) method, is studied for the evaluation of the PFNS for the n(0.5 MeV)+Pu-239 fission reaction and compared to the Kalman filter. The UMC approach to nuclear data evaluation is implemented in a variety of ways to test convergence toward the Kalman filter results and to determine the nonlinearities present in the LA model. Ultimately, the UMC approach is shown to be comparable to the Kalman filter for a realistic data evaluation of the PFNS and is capable of capturing the nonlinearities present in the LA model. Next, the impact that the PFNS uncertainties have on important critical assemblies is investigated. Using the PFNS covariance matrices in the ENDF/B-VII.1 nuclear data library, the uncertainties of the effective multiplication factor, leakage, and spectral indices of the Lady Godiva and Jezebel critical assemblies are quantified. Using principal component analysis on the PFNS covariance matrices results in needing only 2-3 principal components to retain the PFNS uncertainties. Then, using the polynomial chaos expansion (PCE) on the uncertain output
Quantification of Uncertainties in Integrated Spacecraft System Models Project
National Aeronautics and Space Administration — The proposed effort is to investigate a novel uncertainty quantification (UQ) approach based on non-intrusive polynomial chaos (NIPC) for computationally efficient...
Institute of Scientific and Technical Information of China (English)
范梦璇
2015-01-01
<正>Employ change-related uncertainty is a condition that under current continually changing business environment,the organizations also have to change,the change include strategic direction,structure and staffing levels to help company to keep competitive(Armenakis&Bedeian,1999);However;these
DEFF Research Database (Denmark)
Greasley, David; Madsen, Jakob B.
2006-01-01
A severe collapse of fixed capital formation distinguished the onset of the Great Depression from other investment downturns between the world wars. Using a model estimated for the years 1890-2000, we show that the expected profitability of capital measured by Tobin's q, and the uncertainty...
Cettolin, E.; Riedl, A.M.
2013-01-01
An important element for the public support of policies is their perceived justice. At the same time most policy choices have uncertain outcomes. We report the results of a first experiment investigating just allocations of resources when some recipients are exposed to uncertainty. Although, under c
The factualization of uncertainty:
DEFF Research Database (Denmark)
Meyer, G.; Folker, A.P.; Jørgensen, R.B.
2005-01-01
exercises, scientific uncertainty is turned into risk, expressed in facts and figures. Paradoxically, this conveys an impression of certainty, while value-disagreement and conflicts of interest remain hidden below the surface of factuality. Public dialogue and negotiation along these lines are rendered...
Vehicle Routing under Uncertainty
Máhr, T.
2011-01-01
In this thesis, the main focus is on the study of a real-world transportation problem with uncertainties, and on the comparison of a centralized and a distributed solution approach in the context of this problem. We formalize the real-world problem, and provide a general framework to extend it with
Uncertainties in repository modeling
Energy Technology Data Exchange (ETDEWEB)
Wilson, J.R.
1996-12-31
The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling.
Institute of Scientific and Technical Information of China (English)
LIDian-qing; ZHANGSheng-kun
2004-01-01
The classical probability theory cannot effectively quantify the parameter uncertainty in probability of detection.Furthermore,the conventional data analytic method and expert judgment method fail to handle the problem of model uncertainty updating with the information from nondestructive inspection.To overcome these disadvantages,a Bayesian approach was proposed to quantify the parameter uncertainty in probability of detection.Furthermore,the formulae of the multiplication factors to measure the statistical uncertainties in the probability of detection following the Weibull distribution were derived.A Bayesian updating method was applied to compute the posterior probabilities of model weights and the posterior probability density functions of distribution parameters of probability of detection.A total probability model method was proposed to analyze the problem of multi-layered model uncertainty updating.This method was then applied to the problem of multilayered corrosion model uncertainty updating for ship structures.The results indicate that the proposed method is very effective in analyzing the problem of multi-layered model uncertainty updating.
Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions
Crespo, Luis G.; Munoz, Cesar A.; Narkawicz, Anthony J.; Kenny, Sean P.; Giesy, Daniel P.
2011-01-01
This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. A Bernstein expansion approach is used to size hyper-rectangular subsets while a sum of squares programming approach is used to size quasi-ellipsoidal subsets. These methods are applicable to requirement functions whose functional dependency on the uncertainty is a known polynomial. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the uncertainty model assumed (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.
A review of uncertainty propagation in orbital mechanics
Luo, Ya-zhong; Yang, Zhen
2017-02-01
Orbital uncertainty propagation plays an important role in space situational awareness related missions such as tracking and data association, conjunction assessment, sensor resource management and anomaly detection. Linear models and Monte Carlo simulation were primarily used to propagate uncertainties. However, due to the nonlinear nature of orbital dynamics, problems such as low precision and intensive computation have greatly hampered the application of these methods. Aiming at solving these problems, many nonlinear uncertainty propagators have been proposed in the past two decades. To motivate this research area and facilitate the development of orbital uncertainty propagation, this paper summarizes the existing linear and nonlinear uncertainty propagators and their associated applications in the field of orbital mechanics. Frameworks of methods for orbital uncertainty propagation, the advantages and drawbacks of different methods, as well as potential directions for future efforts are also discussed.
Estimating uncertainty of inference for validation
Energy Technology Data Exchange (ETDEWEB)
Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM
2010-09-30
We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the
Traceability and Measurement Uncertainty
DEFF Research Database (Denmark)
Tosello, Guido; De Chiffre, Leonardo
2004-01-01
respects necessary scientific precision and problem-solving approach of the field of engineering studies. Competences should be presented in a way that is methodologically and didactically optimised for employees with a mostly work-based vocational qualification and should at the same time be appealing...... and motivating to this important group. The developed e-learning system consists on 12 different chapters dealing with the following topics: 1. Basics 2. Traceability and measurement uncertainty 3. Coordinate metrology 4. Form measurement 5. Surface testing 6. Optical measurement and testing 7. Measuring rooms 8....... Machine tool testing 9. The role of manufacturing metrology for QM 10. Inspection planning 11. Quality management of measurements incl. Documentation 12. Advanced manufacturing measurement technology The present report (which represents the section 2 - Traceability and Measurement Uncertainty – of the e...
National Aeronautics and Space Administration — ASSURE - Aeroelastic / Aeroservoelastic (AE/ASE) Uncertainty and Reliability Engineering capability - is a set of probabilistic computer programs for isolating...
Aggregating and Communicating Uncertainty.
1980-04-01
means for identifying and communicating uncertainty. i 12- APPENDIX A BIBLIOGRAPHY j| 1. Ajzen , Icek ; "Intuitive Theories of Events and the Effects...disregarding valid but noncausal information." (Icak Ajzen , "Intuitive Theo- ries of Events and the Effects of Base-Rate Information on Prediction...9 4i,* ,4.. -. .- S % to the criterion while disregarding valid but noncausal information." (Icak Ajzen , "Intuitive Theories of Events and the Effects
1981-05-15
Variants of Uncertainty Daniel Kahneman University of British Columbia Amos Tversky Stanford University DTI-C &%E-IECTE ~JUNO 1i 19 8 1j May 15, 1981... Dennett , 1979) in which different parts have ac- cess to different data, assign then different weights and hold different views of the situation...2robable and t..h1 provable. Oxford- Claredor Press, 1977. Dennett , D.C. Brainstorms. Hassocks: Harvester, 1979. Donchin, E., Ritter, W. & McCallum, W.C
Uncertainty in artificial intelligence
Shachter, RD; Henrion, M; Lemmer, JF
1990-01-01
This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language und
Optimizing production under uncertainty
DEFF Research Database (Denmark)
Rasmussen, Svend
This Working Paper derives criteria for optimal production under uncertainty based on the state-contingent approach (Chambers and Quiggin, 2000), and discusses po-tential problems involved in applying the state-contingent approach in a normative context. The analytical approach uses the concept o...... the relative benefits and of using the state-contingent approach in a norma-tive context, compared to the EV model....
Participation under Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Boudourides, Moses A. [Univ. of Patras, Rio-Patras (Greece). Dept. of Mathematics
2003-10-01
This essay reviews a number of theoretical perspectives about uncertainty and participation in the present-day knowledge-based society. After discussing the on-going reconfigurations of science, technology and society, we examine how appropriate for policy studies are various theories of social complexity. Post-normal science is such an example of a complexity-motivated approach, which justifies civic participation as a policy response to an increasing uncertainty. But there are different categories and models of uncertainties implying a variety of configurations of policy processes. A particular role in all of them is played by expertise whose democratization is an often-claimed imperative nowadays. Moreover, we discuss how different participatory arrangements are shaped into instruments of policy-making and framing regulatory processes. As participation necessitates and triggers deliberation, we proceed to examine the role and the barriers of deliberativeness. Finally, we conclude by referring to some critical views about the ultimate assumptions of recent European policy frameworks and the conceptions of civic participation and politicization that they invoke.
Uncertainty in magnetic activity indices
Institute of Scientific and Technical Information of China (English)
XU WenYao
2008-01-01
Magnetic activity indices are widely used in theoretical studies of solar-terrestrial coupling and space weather prediction. However, the indices suffer from various uncertainties, which limit their application and even mislead to incorrect conclu-sion. In this paper we analyze three most popular indices, Kp, AE and Dst. Three categories of uncertainties in magnetic indices are discussed: "data uncertainty" originating from inadequate data processing, "station uncertainty" caused by in-complete station covering, and "physical uncertainty" stemming from unclear physical mechanism. A comparison between magnetic disturbances and related indices indicate that the residual Sq will cause an uncertainty of 1-2 in K meas-urement, the uncertainty in saturated AE is as much as 50%, and the uncertainty in Dst index caused by the partial ring currents is about a half of the partial ring cur-rent.
Uncertainty in magnetic activity indices
Institute of Scientific and Technical Information of China (English)
2008-01-01
Magnetic activity indices are widely used in theoretical studies of solar-terrestrial coupling and space weather prediction. However, the indices suffer from various uncertainties, which limit their application and even mislead to incorrect conclu-sion. In this paper we analyze three most popular indices, Kp, AE and Dst. Three categories of uncertainties in magnetic indices are discussed: "data uncertainty" originating from inadequate data processing, "station uncertainty" caused by in-complete station covering, and "physical uncertainty" stemming from unclear physical mechanism. A comparison between magnetic disturbances and related indices indicate that the residual Sq will cause an uncertainty of 1―2 in K meas-urement, the uncertainty in saturated AE is as much as 50%, and the uncertainty in Dst index caused by the partial ring currents is about a half of the partial ring cur-rent.
Transforming Binary Uncertainties for Robust Speech Recognition
2006-08-01
1-0117), an AFRL grant via Veridian and an NSF grant (IIS-0534707). We thank A. Acero and M. L. Seltzer for helpful suggestions. A preliminary...Deng, J. Droppo, and A. Acero , “Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of...amplitude modulation,” IEEE Trans. on Neural Networks, vol. 15, pp. 1135–1150, 2004. [16] X. Huang, A. Acero , and H. Hon, Spoken Language Processing
Pauli effects in uncertainty relations
Toranzo, I V; Esquivel, R O; Dehesa, J S
2014-01-01
In this letter we analyze the effect of the spin dimensionality of a physical system in two mathematical formulations of the uncertainty principle: a generalized Heisenberg uncertainty relation valid for all antisymmetric N-fermion wavefunctions, and the Fisher-information- based uncertainty relation valid for all antisymmetric N-fermion wavefunctions of central potentials. The accuracy of these spin-modified uncertainty relations is examined for all atoms from Hydrogen to Lawrencium in a self-consistent framework.
Uncertainty Quantification in Climate Modeling and Projection
Energy Technology Data Exchange (ETDEWEB)
Qian, Yun; Jackson, Charles; Giorgi, Filippo; Booth, Ben; Duan, Qingyun; Forest, Chris; Higdon, Dave; Hou, Z. Jason; Huerta, Gabriel
2016-05-01
The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change information for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluation of uncertainties in climate projections, such as the effects of uncertain initial and boundary conditions, uncertain physics, and limitations of observational records. Progress in quantitatively estimating uncertainties in hydrologic, land surface, and atmospheric models at both regional and global scales was also reviewed. The application of Uncertainty Quantification (UQ) concepts to coupled climate system models is still in its infancy. The Coupled Model Intercomparison Project (CMIP) multi-model ensemble currently represents the primary data for
Gravitational tests of the generalized uncertainty principle
Energy Technology Data Exchange (ETDEWEB)
Scardigli, Fabio [American University of the Middle East, Department of Mathematics, College of Engineering, P.O. Box 220, Dasman (Kuwait); Politecnico di Milano, Dipartimento di Matematica, Milan (Italy); Casadio, Roberto [Alma Mater Universita di Bologna, Dipartimento di Fisica e Astronomia, Bologna (Italy); INFN, Sezione di Bologna, Bologna (Italy)
2015-09-15
We compute the corrections to the Schwarzschild metric necessary to reproduce the Hawking temperature derived from a generalized uncertainty principle (GUP), so that the GUP deformation parameter is directly linked to the deformation of the metric. Using this modified Schwarzschild metric, we compute corrections to the standard general relativistic predictions for the light deflection and perihelion precession, both for planets in the solar system and for binary pulsars. This analysis allows us to set bounds for the GUP deformation parameter from well-known astronomical measurements. (orig.)
Collective Uncertainty Entanglement Test
Rudnicki, Łukasz; Życzkowski, Karol
2011-01-01
For a given pure state of a composite quantum system we analyze the product of its projections onto a set of locally orthogonal separable pure states. We derive a bound for this product analogous to the entropic uncertainty relations. For bipartite systems the bound is saturated for maximally entangled states and it allows us to construct a family of entanglement measures, we shall call collectibility. As these quantities are experimentally accessible, the approach advocated contributes to the task of experimental quantification of quantum entanglement, while for a three-qubit system it is capable to identify the genuine three-party entanglement.
Mathematical Analysis of Uncertainty
Directory of Open Access Journals (Sweden)
Angel GARRIDO
2016-01-01
Full Text Available Classical Logic showed early its insufficiencies for solving AI problems. The introduction of Fuzzy Logic aims at this problem. There have been research in the conventional Rough direction alone or in the Fuzzy direction alone, and more recently, attempts to combine both into Fuzzy Rough Sets or Rough Fuzzy Sets. We analyse some new and powerful tools in the study of Uncertainty, as the Probabilistic Graphical Models, Chain Graphs, Bayesian Networks, and Markov Networks, integrating our knowledge of graphs and probability.
Optimizing production under uncertainty
DEFF Research Database (Denmark)
Rasmussen, Svend
This Working Paper derives criteria for optimal production under uncertainty based on the state-contingent approach (Chambers and Quiggin, 2000), and discusses po-tential problems involved in applying the state-contingent approach in a normative context. The analytical approach uses the concept...... of state-contingent production functions and a definition of inputs including both sort of input, activity and alloca-tion technology. It also analyses production decisions where production is combined with trading in state-contingent claims such as insurance contracts. The final part discusses...
Variance-based uncertainty relations
Huang, Yichen
2010-01-01
It is hard to overestimate the fundamental importance of uncertainty relations in quantum mechanics. In this work, I propose state-independent variance-based uncertainty relations for arbitrary observables in both finite and infinite dimensional spaces. We recover the Heisenberg uncertainty principle as a special case. By studying examples, we find that the lower bounds provided by our new uncertainty relations are optimal or near-optimal. I illustrate the uses of our new uncertainty relations by showing that they eliminate one common obstacle in a sequence of well-known works in entanglement detection, and thus make these works much easier to access in applications.
mu analysis with real parametric uncertainty
Young, Peter M.; Newlin, Matthew P.; Doyle, John C.
1991-01-01
The authors give a broad overview, from a LFT (linear fractional transformation)/mu perspective, of some of the theoretical and practical issues associated with robustness in the presence of real parametric uncertainty, with a focus on computation. Recent results on the properties of mu in the mixed case are reviewed, including issues of NP completeness, continuity, computation of bounds, the equivalence of mu and its bounds, and some direct comparisons with Kharitonov-type analysis methods. In addition, some advances in the computational aspects of the problem, including a novel branch and bound algorithm, are briefly presented together with numerical results. The results suggest that while the mixed mu problem may have inherently combinatoric worst-case behavior, practical algorithms with modest computational requirements can be developed for problems of medium size (less than 100 parameters) that are of engineering interest.
Uncertainty Analysis of Light Water Reactor Fuel Lattices
Directory of Open Access Journals (Sweden)
C. Arenas
2013-01-01
Full Text Available The study explored the calculation of uncertainty based on available cross-section covariance data and computational tool on fuel lattice levels, which included pin cell and the fuel assembly models. Uncertainty variations due to temperatures changes and different fuel compositions are the main focus of this analysis. Selected assemblies and unit pin cells were analyzed according to the OECD LWR UAM benchmark specifications. Criticality and uncertainty analysis were performed using TSUNAMI-2D sequence in SCALE 6.1. It was found that uncertainties increase with increasing temperature, while kinf decreases. This increase in the uncertainty is due to the increase in sensitivity of the largest contributing reaction of uncertainty, namely, the neutron capture reaction 238U(n, γ due to the Doppler broadening. In addition, three types (UOX, MOX, and UOX-Gd2O3 of fuel material compositions were analyzed. A remarkable increase in uncertainty in kinf was observed for the case of MOX fuel. The increase in uncertainty of kinf in MOX fuel was nearly twice the corresponding value in UOX fuel. The neutron-nuclide reaction of 238U, mainly inelastic scattering (n, n′, contributed the most to the uncertainties in the MOX fuel, shifting the neutron spectrum to higher energy compared to the UOX fuel.
Integrating Out Astrophysical Uncertainties
Fox, Patrick J; Weiner, Neal
2010-01-01
Underground searches for dark matter involve a complicated interplay of particle physics, nuclear physics, atomic physics and astrophysics. We attempt to remove the uncertainties associated with astrophysics by developing the means to map the observed signal in one experiment directly into a predicted rate at another. We argue that it is possible to make experimental comparisons that are completely free of astrophysical uncertainties by focusing on {\\em integral} quantities, such as $g(v_{min})=\\int_{v_{min}} dv\\, f(v)/v $ and $\\int_{v_{thresh}} dv\\, v g(v)$. Direct comparisons are possible when the $v_{min}$ space probed by different experiments overlap. As examples, we consider the possible dark matter signals at CoGeNT, DAMA and CRESST-Oxygen. We find that expected rate from CoGeNT in the XENON10 experiment is higher than observed, unless scintillation light output is low. Moreover, we determine that S2-only analyses are constraining, unless the charge yield $Q_y< 2.4 {\\, \\rm electrons/keV}$. For DAMA t...
Application of XSUSA with aleatoric and epistemic uncertainties
Energy Technology Data Exchange (ETDEWEB)
Gallner, Lucia; Klein, Markus; Krzykacz-Hausmann, Bernard; Pautz, Andreas; Velkov, Kiril; Zwermann, Winfried [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) mbH, Garching (Germany). Forschungszentrum
2012-11-01
When performing sampling based uncertainty and sensitivity analyses for neutron transport problems with the Monte Carlo method, two kinds of uncertainties have to be considered, namely aleatoric uncertainties arising from the stochastic nature of the simulation procedure, and epistemic uncertainties arising from an incomplete knowledge of the values of input parameters. To determine the influence of the epistemic uncertainties alone, the sample calculations from epistemic sampling can traditionally be performed with a very large number of histories such that the aleatoric uncertainties become negligible and the total uncertainty practically only comes from the influence of the epistemic uncertainties. This procedure may be CPU time intensive. In the present paper, a method was applied which uses heavily reduced numbers of particle histories in each sample calculation, and, nevertheless, is able to largely eliminate the aleatoric uncertainty contribution introduced to the output. Applying this approach, sampling based uncertainty and sensitivity analyses with nuclear covariance data were performed with the XSUSA code and KENO-Va from the SCALE 6 system as Monte Carlo transport solver, for an international criticality benchmark. Equivalent uncertainty and sensitivity results were obtained as compared to the traditional method of using very large numbers of histories in each sample calculation. Thereby, computing times could be reduced by factors of the magnitude of 100. The use of multi-group nuclear data is no restriction, i.e. the described method can also be applied when using continuous energy nuclear data. The method can equally well be used for analyses with a Monte Carlo transport solver and epistemic uncertainties from other sources, like manufacturing tolerances. So far, the method was applied to stand-alone Monte Carlo criticality calculations; currently, investigations are being performed with calculations coupling Monte Carlo transport with depletion
Uncertainty Quantification for Cargo Hold Fires
DeGennaro, Anthony M; Martinelli, Luigi; Rowley, Clarence W
2015-01-01
The purpose of this study is twofold -- first, to introduce the application of high-order discontinuous Galerkin methods to buoyancy-driven cargo hold fire simulations, second, to explore statistical variation in the fluid dynamics of a cargo hold fire given parameterized uncertainty in the fire source location and temperature. Cargo hold fires represent a class of problems that require highly-accurate computational methods to simulate faithfully. Hence, we use an in-house discontinuous Galerkin code to treat these flows. Cargo hold fires also exhibit a large amount of uncertainty with respect to the boundary conditions. Thus, the second aim of this paper is to quantify the resulting uncertainty in the flow, using tools from the uncertainty quantification community to ensure that our efforts require a minimal number of simulations. We expect that the results of this study will provide statistical insight into the effects of fire location and temperature on cargo fires, and also assist in the optimization of f...
Environmental adversity and uncertainty favour cooperation
Andras, Peter; Lazarus, John; Roberts, Gilbert
2007-01-01
Background A major cornerstone of evolutionary biology theory is the explanation of the emergence of cooperation in communities of selfish individuals. There is an unexplained tendency in the plant and animal world – with examples from alpine plants, worms, fish, mole-rats, monkeys and humans – for cooperation to flourish where the environment is more adverse (harsher) or more unpredictable. Results Using mathematical arguments and computer simulations we show that in more adverse environments individuals perceive their resources to be more unpredictable, and that this unpredictability favours cooperation. First we show analytically that in a more adverse environment the individual experiences greater perceived uncertainty. Second we show through a simulation study that more perceived uncertainty implies higher level of cooperation in communities of selfish individuals. Conclusion This study captures the essential features of the natural examples: the positive impact of resource adversity or uncertainty on cooperation. These newly discovered connections between environmental adversity, uncertainty and cooperation help to explain the emergence and evolution of cooperation in animal and human societies. PMID:18053138
LCA data quality: sensitivity and uncertainty analysis.
Guo, M; Murphy, R J
2012-10-01
Life cycle assessment (LCA) data quality issues were investigated by using case studies on products from starch-polyvinyl alcohol based biopolymers and petrochemical alternatives. The time horizon chosen for the characterization models was shown to be an important sensitive parameter for the environmental profiles of all the polymers. In the global warming potential and the toxicity potential categories the comparison between biopolymers and petrochemical counterparts altered as the time horizon extended from 20 years to infinite time. These case studies demonstrated that the use of a single time horizon provide only one perspective on the LCA outcomes which could introduce an inadvertent bias into LCA outcomes especially in toxicity impact categories and thus dynamic LCA characterization models with varying time horizons are recommended as a measure of the robustness for LCAs especially comparative assessments. This study also presents an approach to integrate statistical methods into LCA models for analyzing uncertainty in industrial and computer-simulated datasets. We calibrated probabilities for the LCA outcomes for biopolymer products arising from uncertainty in the inventory and from data variation characteristics this has enabled assigning confidence to the LCIA outcomes in specific impact categories for the biopolymer vs. petrochemical polymer comparisons undertaken. Uncertainty combined with the sensitivity analysis carried out in this study has led to a transparent increase in confidence in the LCA findings. We conclude that LCAs lacking explicit interpretation of the degree of uncertainty and sensitivities are of limited value as robust evidence for decision making or comparative assertions.
Environmental adversity and uncertainty favour cooperation
Directory of Open Access Journals (Sweden)
Lazarus John
2007-11-01
Full Text Available Abstract Background A major cornerstone of evolutionary biology theory is the explanation of the emergence of cooperation in communities of selfish individuals. There is an unexplained tendency in the plant and animal world – with examples from alpine plants, worms, fish, mole-rats, monkeys and humans – for cooperation to flourish where the environment is more adverse (harsher or more unpredictable. Results Using mathematical arguments and computer simulations we show that in more adverse environments individuals perceive their resources to be more unpredictable, and that this unpredictability favours cooperation. First we show analytically that in a more adverse environment the individual experiences greater perceived uncertainty. Second we show through a simulation study that more perceived uncertainty implies higher level of cooperation in communities of selfish individuals. Conclusion This study captures the essential features of the natural examples: the positive impact of resource adversity or uncertainty on cooperation. These newly discovered connections between environmental adversity, uncertainty and cooperation help to explain the emergence and evolution of cooperation in animal and human societies.
On the worst case uncertainty and its evaluation
Fabbiano, L.; Giaquinto, N.; Savino, M.; Vacca, G.
2016-02-01
The paper is a review on the worst case uncertainty (WCU) concept, neglected in the Guide to the Expression of Uncertainty in Measurements (GUM), but necessary for a correct uncertainty assessment in a number of practical cases involving distribution with compact support. First, it is highlighted that the knowledge of the WCU is necessary to choose a sensible coverage factor, associated to a sensible coverage probability: the Maximum Acceptable Coverage Factor (MACF) is introduced as a convenient index to guide this choice. Second, propagation rules for the worst-case uncertainty are provided in matrix and scalar form. It is highlighted that when WCU propagation cannot be computed, the Monte Carlo approach is the only way to obtain a correct expanded uncertainty assessment, in contrast to what can be inferred from the GUM. Third, examples of applications of the formulae to ordinary instruments and measurements are given. Also an example taken from the GUM is discussed, underlining some inconsistencies in it.
Pragmatic aspects of uncertainty propagation: A conceptual review
Thacker, W.Carlisle
2015-09-11
When quantifying the uncertainty of the response of a computationally costly oceanographic or meteorological model stemming from the uncertainty of its inputs, practicality demands getting the most information using the fewest simulations. It is widely recognized that, by interpolating the results of a small number of simulations, results of additional simulations can be inexpensively approximated to provide a useful estimate of the variability of the response. Even so, as computing the simulations to be interpolated remains the biggest expense, the choice of these simulations deserves attention. When making this choice, two requirement should be considered: (i) the nature of the interpolation and ii) the available information about input uncertainty. Examples comparing polynomial interpolation and Gaussian process interpolation are presented for three different views of input uncertainty.
Pragmatic aspects of uncertainty propagation: A conceptual review
Thacker, W. Carlisle; Iskandarani, Mohamed; Gonçalves, Rafael C.; Srinivasan, Ashwanth; Knio, Omar M.
2015-11-01
When quantifying the uncertainty of the response of a computationally costly oceanographic or meteorological model stemming from the uncertainty of its inputs, practicality demands getting the most information using the fewest simulations. It is widely recognized that, by interpolating the results of a small number of simulations, results of additional simulations can be inexpensively approximated to provide a useful estimate of the variability of the response. Even so, as computing the simulations to be interpolated remains the biggest expense, the choice of these simulations deserves attention. When making this choice, two requirement should be considered: (i) the nature of the interpolation and (ii) the available information about input uncertainty. Examples comparing polynomial interpolation and Gaussian process interpolation are presented for three different views of input uncertainty.
Scientific visualization uncertainty, multifield, biomedical, and scalable visualization
Chen, Min; Johnson, Christopher; Kaufman, Arie; Hagen, Hans
2014-01-01
Based on the seminar that took place in Dagstuhl, Germany in June 2011, this contributed volume studies the four important topics within the scientific visualization field: uncertainty visualization, multifield visualization, biomedical visualization and scalable visualization. • Uncertainty visualization deals with uncertain data from simulations or sampled data, uncertainty due to the mathematical processes operating on the data, and uncertainty in the visual representation, • Multifield visualization addresses the need to depict multiple data at individual locations and the combination of multiple datasets, • Biomedical is a vast field with select subtopics addressed from scanning methodologies to structural applications to biological applications, • Scalability in scientific visualization is critical as data grows and computational devices range from hand-held mobile devices to exascale computational platforms. Scientific Visualization will be useful to practitioners of scientific visualization, ...
Uncertainty relation in Schwarzschild spacetime
Feng, Jun; Zhang, Yao-Zhong; Gould, Mark D.; Fan, Heng
2015-04-01
We explore the entropic uncertainty relation in the curved background outside a Schwarzschild black hole, and find that Hawking radiation introduces a nontrivial modification on the uncertainty bound for particular observer, therefore it could be witnessed by proper uncertainty game experimentally. We first investigate an uncertainty game between a free falling observer and his static partner holding a quantum memory initially entangled with the quantum system to be measured. Due to the information loss from Hawking decoherence, we find an inevitable increase of the uncertainty on the outcome of measurements in the view of static observer, which is dependent on the mass of the black hole, the distance of observer from event horizon, and the mode frequency of quantum memory. To illustrate the generality of this paradigm, we relate the entropic uncertainty bound with other uncertainty probe, e.g., time-energy uncertainty. In an alternative game between two static players, we show that quantum information of qubit can be transferred to quantum memory through a bath of fluctuating quantum fields outside the black hole. For a particular choice of initial state, we show that the Hawking decoherence cannot counteract entanglement generation after the dynamical evolution of system, which triggers an effectively reduced uncertainty bound that violates the intrinsic limit -log2 c. Numerically estimation for a proper choice of initial state shows that our result is comparable with possible real experiments. Finally, a discussion on the black hole firewall paradox in the context of entropic uncertainty relation is given.
An uncertainty inventory demonstration - a primary step in uncertainty quantification
Energy Technology Data Exchange (ETDEWEB)
Langenbrunner, James R. [Los Alamos National Laboratory; Booker, Jane M [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Salazar, Issac F [Los Alamos National Laboratory; Ross, Timothy J [UNM
2009-01-01
Tools, methods, and theories for assessing and quantifying uncertainties vary by application. Uncertainty quantification tasks have unique desiderata and circumstances. To realistically assess uncertainty requires the engineer/scientist to specify mathematical models, the physical phenomena of interest, and the theory or framework for assessments. For example, Probabilistic Risk Assessment (PRA) specifically identifies uncertainties using probability theory, and therefore, PRA's lack formal procedures for quantifying uncertainties that are not probabilistic. The Phenomena Identification and Ranking Technique (PIRT) proceeds by ranking phenomena using scoring criteria that results in linguistic descriptors, such as importance ranked with words, 'High/Medium/Low.' The use of words allows PIRT to be flexible, but the analysis may then be difficult to combine with other uncertainty theories. We propose that a necessary step for the development of a procedure or protocol for uncertainty quantification (UQ) is the application of an Uncertainty Inventory. An Uncertainty Inventory should be considered and performed in the earliest stages of UQ.
2012-03-01
certify to : ISO 9001 (QMS), ISO 14001 (EMS), TS 16949 (US Automotive) etc. 2 3 DoD QSM 4.2 standard ISO /IEC 17025:2005 Each has uncertainty...298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Laboratories – ISO /IEC 17025 Inspection Bodies – ISO /IEC 17020 RMPs – ISO Guide 34 (Reference...Materials) PT Providers – ISO 17043 Product Certifiers – ISO Guide 65 Government Programs: DoD ELAP, EPA Energy Star, CPSC Toy Safety, NRC, NIST
Sustainability and uncertainty
DEFF Research Database (Denmark)
Jensen, Karsten Klint
2007-01-01
The widely used concept of sustainability is seldom precisely defined, and its clarification involves making up one's mind about a range of difficult questions. One line of research (bottom-up) takes sustaining a system over time as its starting point and then infers prescriptions from...... this requirement. Another line (top-down) takes an economical interpretation of the Brundtland Commission's suggestion that the present generation's needsatisfaction should not compromise the need-satisfaction of future generations as its starting point. It then measures sustainability at the level of society...... a clarified ethical goal, disagreements can arise. At present we do not know what substitutions will be possible in the future. This uncertainty clearly affects the prescriptions that follow from the measure of sustainability. Consequently, decisions about how to make future agriculture sustainable...
Risk, Uncertainty, and Entrepreneurship
DEFF Research Database (Denmark)
Koudstaal, Martin; Sloof, Randolph; Van Praag, Mirjam
2016-01-01
Theory predicts that entrepreneurs have distinct attitudes toward risk and uncertainty, but empirical evidence is mixed. To better understand these mixed results, we perform a large “lab-in-the-field” experiment comparing entrepreneurs to managers (a suitable comparison group) and employees (n D...... 21288). The results indicate that entrepreneurs perceive themselves as less risk averse than managers and employees, in line with common wisdom. However, when using experimental incentivized measures, the differences are subtler. Entrepreneurs are only found to be unique in their lower degree of loss...... aversion, and not in their risk or ambiguity aversion. This combination of results might be explained by our finding that perceived risk attitude is not only correlated to risk aversion but also to loss aversion. Overall, we therefore suggest using a broader definition of risk that captures this unique...
Risk, Uncertainty and Entrepreneurship
DEFF Research Database (Denmark)
Koudstaal, Martin; Sloof, Randolph; Van Praag, Mirjam
Theory predicts that entrepreneurs have distinct attitudes towards risk and uncertainty, but empirical evidence is mixed. To better understand the unique behavioral characteristics of entrepreneurs and the causes of these mixed results, we perform a large ‘lab-in-the-field’ experiment comparing...... entrepreneurs to managers – a suitable comparison group – and employees (n = 2288). The results indicate that entrepreneurs perceive themselves as less risk averse than managers and employees, in line with common wisdom. However, when using experimental incentivized measures, the differences are subtler....... Entrepreneurs are only found to be unique in their lower degree of loss aversion, and not in their risk or ambiguity aversion. This combination of results might be explained by our finding that perceived risk attitude is not only correlated to risk aversion but also to loss aversion. Overall, we therefore...
Generalized uncertainty relations
Herdegen, Andrzej; Ziobro, Piotr
2017-04-01
The standard uncertainty relations (UR) in quantum mechanics are typically used for unbounded operators (like the canonical pair). This implies the need for the control of the domain problems. On the other hand, the use of (possibly bounded) functions of basic observables usually leads to more complex and less readily interpretable relations. In addition, UR may turn trivial for certain states if the commutator of observables is not proportional to a positive operator. In this letter we consider a generalization of standard UR resulting from the use of two, instead of one, vector states. The possibility to link these states to each other in various ways adds additional flexibility to UR, which may compensate some of the above-mentioned drawbacks. We discuss applications of the general scheme, leading not only to technical improvements, but also to interesting new insight.
Medical decisions under uncertainty.
Carmi, A
1993-01-01
The court applies the criteria of the reasonable doctor and common practice in order to consider the behaviour of a defendant physician. The meaning of our demand that the doctor expects that his or her acts or omissions will bring about certain implications is that, according to the present circumstances and subject to the limited knowledge of the common practice, the course of certain events or situations in the future may be assumed in spite of the fog of uncertainty which surrounds us. The miracles and wonders of creation are concealed from us, and we are not aware of the way and the nature of our bodily functioning. Therefore, there seems to be no way to avoid mistakes, because in several cases the correct diagnosis cannot be determined even with the most advanced application of all information available. Doctors find it difficult to admit that they grope in the dark. They wish to form clear and accurate diagnoses for their patients. The fact that their profession is faced with innumerable and unavoidable risks and mistakes is hard to swallow, and many of them claim that in their everyday work this does not happen. They should not content themselves by changing their style. A radical metamorphosis is needed. They should not be tempted to formulate their diagnoses in 'neutral' statements in order to be on the safe side. Uncertainty should be accepted and acknowledged by the profession and by the public at large as a human phenomenon, as an integral part of any human decision, and as a clear characteristic of any legal or medical diagnosis.(ABSTRACT TRUNCATED AT 250 WORDS)
Relational uncertainty in service dyads
DEFF Research Database (Denmark)
Kreye, Melanie
2016-01-01
Purpose: Relational uncertainty determines how relationships develop because it enables the building of trust and commitment. However, relational uncertainty has not been explored in an inter-organisational setting. This paper investigates how organisations experience relational uncertainty...... via semi-structured interviews and secondary data. Findings: The findings suggest that relational uncertainty is caused by the partner’s unresolved organisational uncertainty, i.e. their lacking capabilities to deliver or receive (parts of) the service. Furthermore, we found that resolving...... and explain the actions of a partnering organisation due to a lack of knowledge about their abilities and intentions. Second, we present suitable organisational responses to relational uncertainty and their effect on service quality....
The Uncertainties of Risk Management
DEFF Research Database (Denmark)
Vinnari, Eija; Skærbæk, Peter
2014-01-01
Purpose – The purpose of this paper is to analyse the implementation of risk management as a tool for internal audit activities, focusing on unexpected effects or uncertainties generated during its application. Design/methodology/approach – Public and confidential documents as well as semi......-structured interviews are analysed through the lens of actor-network theory to identify the effects of risk management devices in a Finnish municipality. Findings – The authors found that risk management, rather than reducing uncertainty, itself created unexpected uncertainties that would otherwise not have emerged....... These include uncertainties relating to legal aspects of risk management solutions, in particular the issue concerning which types of document are considered legally valid; uncertainties relating to the definition and operationalisation of risk management; and uncertainties relating to the resources available...
Impact of discharge data uncertainty on nutrient load uncertainty
Westerberg, Ida; Gustavsson, Hanna; Sonesten, Lars
2016-04-01
Uncertainty in the rating-curve model of the stage-discharge relationship leads to uncertainty in discharge time series. These uncertainties in turn affect many other analyses based on discharge data, such as nutrient load estimations. It is important to understand how large the impact of discharge data uncertainty is on such analyses, since they are often used as the basis to take important environmental management decisions. In the Baltic Sea basin, nutrient load estimates from river mouths are a central information basis for managing and reducing eutrophication in the Baltic Sea. In this study we investigated rating curve uncertainty and its propagation to discharge data uncertainty and thereafter to uncertainty in the load of phosphorous and nitrogen for twelve Swedish river mouths. We estimated rating curve uncertainty using the Voting Point method, which accounts for random and epistemic errors in the stage-discharge relation and allows drawing multiple rating-curve realisations consistent with the total uncertainty. We sampled 40,000 rating curves, and for each sampled curve we calculated a discharge time series from 15-minute water level data for the period 2005-2014. Each discharge time series was then aggregated to daily scale and used to calculate the load of phosphorous and nitrogen from linearly interpolated monthly water samples, following the currently used methodology for load estimation. Finally the yearly load estimates were calculated and we thus obtained distributions with 40,000 load realisations per year - one for each rating curve. We analysed how the rating curve uncertainty propagated to the discharge time series at different temporal resolutions, and its impact on the yearly load estimates. Two shorter periods of daily water quality sampling around the spring flood peak allowed a comparison of load uncertainty magnitudes resulting from discharge data with those resulting from the monthly water quality sampling.
Uncertainty analysis in acoustic investigations
2013-01-01
The problem of uncertainty assessment in acoustic investigations is presented in the hereby paper. The aspect of the uncertainty asymmetry in processing of data obtained in the measuring test of sound levels, determined in decibels, was sketched. On the basis of the analysis of data obtained in the continuous monitoring of road traffic noise in Krakow typical probability distributions for a day, evening and night were determined. The method of the uncertainty assessment based on the propagati...
Dealing with Uncertainties in Initial Orbit Determination
Armellin, Roberto; Di Lizia, Pierluigi; Zanetti, Renato
2015-01-01
A method to deal with uncertainties in initial orbit determination (IOD) is presented. This is based on the use of Taylor differential algebra (DA) to nonlinearly map the observation uncertainties from the observation space to the state space. When a minimum set of observations is available DA is used to expand the solution of the IOD problem in Taylor series with respect to measurement errors. When more observations are available high order inversion tools are exploited to obtain full state pseudo-observations at a common epoch. The mean and covariance of these pseudo-observations are nonlinearly computed by evaluating the expectation of high order Taylor polynomials. Finally, a linear scheme is employed to update the current knowledge of the orbit. Angles-only observations are considered and simplified Keplerian dynamics adopted to ease the explanation. Three test cases of orbit determination of artificial satellites in different orbital regimes are presented to discuss the feature and performances of the proposed methodology.
On Uncertainties in Successive Measurements
Distler, Jacques
2012-01-01
When you measure an observable, A, in Quantum Mechanics, the state of the system changes. This, in turn, affects the quantum-mechanical uncertainty in some non-commuting observable, B. The standard Uncertainty Relation puts a lower bound on the uncertainty of B in the initial state. What is relevant for a subsequent measurement of B, however, is the uncertainty of B in the post-measurement state. We make some remarks on the latter problem, both in the case where A has a pure point spectrum and in the case where A has a continuous spectrum.
Evaluating uncertainty in simulation models
Energy Technology Data Exchange (ETDEWEB)
McKay, M.D.; Beckman, R.J.; Morrison, J.D.; Upton, S.C.
1998-12-01
The authors discussed some directions for research and development of methods for assessing simulation variability, input uncertainty, and structural model uncertainty. Variance-based measures of importance for input and simulation variables arise naturally when using the quadratic loss function of the difference between the full model prediction y and the restricted prediction {tilde y}. The concluded that generic methods for assessing structural model uncertainty do not now exist. However, methods to analyze structural uncertainty for particular classes of models, like discrete event simulation models, may be attainable.
Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint
Energy Technology Data Exchange (ETDEWEB)
Campanelli, Mark; Duck, Benjamin; Emery, Keith
2015-09-28
Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.
Spatial uncertainty model for visual features using a Kinect™ sensor.
Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong
2012-01-01
This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor
Directory of Open Access Journals (Sweden)
Jae-Han Park
2012-06-01
Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Are models, uncertainty, and dispute resolution compatible?
Anderson, J. D.; Wilson, J. L.
2013-12-01
Models and their uncertainty often move from an objective use in planning and decision making into the regulatory environment, then sometimes on to dispute resolution through litigation or other legal forums. Through this last transition whatever objectivity the models and uncertainty assessment may have once possessed becomes biased (or more biased) as each party chooses to exaggerate either the goodness of a model, or its worthlessness, depending on which view is in its best interest. If worthlessness is desired, then what was uncertain becomes unknown, or even unknowable. If goodness is desired, then precision and accuracy are often exaggerated and uncertainty, if it is explicitly recognized, encompasses only some parameters or conceptual issues, ignores others, and may minimize the uncertainty that it accounts for. In dispute resolution, how well is the adversarial process able to deal with these biases? The challenge is that they are often cloaked in computer graphics and animations that appear to lend realism to what could be mostly fancy, or even a manufactured outcome. While junk science can be challenged through appropriate motions in federal court, and in most state courts, it not unusual for biased or even incorrect modeling results, or conclusions based on incorrect results, to be permitted to be presented at trial. Courts allow opinions that are based on a "reasonable degree of scientific certainty," but when that 'certainty' is grossly exaggerated by an expert, one way or the other, how well do the courts determine that someone has stepped over the line? Trials are based on the adversary system of justice, so opposing and often irreconcilable views are commonly allowed, leaving it to the judge or jury to sort out the truth. Can advances in scientific theory and engineering practice, related to both modeling and uncertainty, help address this situation and better ensure that juries and judges see more objective modeling results, or at least see
Optimizing Integrated Terminal Airspace Operations Under Uncertainty
Bosson, Christabelle; Xue, Min; Zelinski, Shannon
2014-01-01
In the terminal airspace, integrated departures and arrivals have the potential to increase operations efficiency. Recent research has developed geneticalgorithm- based schedulers for integrated arrival and departure operations under uncertainty. This paper presents an alternate method using a machine jobshop scheduling formulation to model the integrated airspace operations. A multistage stochastic programming approach is chosen to formulate the problem and candidate solutions are obtained by solving sample average approximation problems with finite sample size. Because approximate solutions are computed, the proposed algorithm incorporates the computation of statistical bounds to estimate the optimality of the candidate solutions. A proof-ofconcept study is conducted on a baseline implementation of a simple problem considering a fleet mix of 14 aircraft evolving in a model of the Los Angeles terminal airspace. A more thorough statistical analysis is also performed to evaluate the impact of the number of scenarios considered in the sampled problem. To handle extensive sampling computations, a multithreading technique is introduced.
Climate model uncertainty vs. conceptual geological uncertainty in hydrological modeling
Directory of Open Access Journals (Sweden)
T. O. Sonnenborg
2015-04-01
Full Text Available Projections of climate change impact are associated with a cascade of uncertainties including CO2 emission scenario, climate model, downscaling and impact model. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project forced by the same CO2 scenario (A1B. The changes from the reference period (1991–2010 to the future period (2081–2100 in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty on the climate models is more important for groundwater hydraulic heads and stream flow.
Extended Forward Sensitivity Analysis for Uncertainty Quantification
Energy Technology Data Exchange (ETDEWEB)
Haihua Zhao; Vincent A. Mousseau
2011-09-01
Verification and validation (V&V) are playing more important roles to quantify uncertainties and realize high fidelity simulations in engineering system analyses, such as transients happened in a complex nuclear reactor system. Traditional V&V in the reactor system analysis focused more on the validation part or did not differentiate verification and validation. The traditional approach to uncertainty quantification is based on a 'black box' approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. The 'black box' method mixes numerical errors with all other uncertainties. It is also not efficient to perform sensitivity analysis. Contrary to the 'black box' method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In these types of approaches equations for the propagation of uncertainty are constructed and the sensitivities are directly solved for as variables in the simulation. This paper presents the forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical
Improved Matrix Uncertainty Selector
Rosenbaum, Mathieu
2011-01-01
We consider the regression model with observation error in the design: y=X\\theta* + e, Z=X+N. Here the random vector y in R^n and the random n*p matrix Z are observed, the n*p matrix X is unknown, N is an n*p random noise matrix, e in R^n is a random noise vector, and \\theta* is a vector of unknown parameters to be estimated. We consider the setting where the dimension p can be much larger than the sample size n and \\theta* is sparse. Because of the presence of the noise matrix N, the commonly used Lasso and Dantzig selector are unstable. An alternative procedure called the Matrix Uncertainty (MU) selector has been proposed in Rosenbaum and Tsybakov (2010) in order to account for the noise. The properties of the MU selector have been studied in Rosenbaum and Tsybakov (2010) for sparse \\theta* under the assumption that the noise matrix N is deterministic and its values are small. In this paper, we propose a modification of the MU selector when N is a random matrix with zero-mean entries having the variances th...
Uncertainties in Arctic Precipitation
Majhi, I.; Alexeev, V. A.; Cherry, J. E.; Cohen, J. L.; Groisman, P. Y.
2012-12-01
Arctic precipitation is riddled with measurement biases; to address the problem is imperative. Our study focuses on comparison of various datasets and analyzing their biases for the region of Siberia and caution that is needed when using them. Five sources of data were used ranging from NOAA's product (RAW, Bogdanova's correction), Yang's correction technique and two reanalysis products (ERA-Interim and NCEP). The reanalysis dataset performed better for some months in comparison to Yang's product, which tends to overestimate precipitation, and the raw dataset, which tends to underestimate. The sources of bias vary from topography, to wind, to missing data .The final three products chosen show higher biases during the winter and spring season. Emphasis on equations which incorporate blizzards, blowing snow and higher wind speed is necessary for regions which are influenced by any or all of these factors; Bogdanova's correction technique is the most robust of all the datasets analyzed and gives the most reasonable results. One of our future goals is to analyze the impact of precipitation uncertainties on water budget analysis for the Siberian Rivers.
Mama Software Features: Uncertainty Testing
Energy Technology Data Exchange (ETDEWEB)
Ruggiero, Christy E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Porter, Reid B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-05-30
This document reviews how the uncertainty in the calculations is being determined with test image data. The results of this testing give an ‘initial uncertainty’ number than can be used to estimate the ‘back end’ uncertainty in digital image quantification in images. Statisticians are refining these numbers as part of a UQ effort.
Uncertainty in Integrated Assessment Scenarios
Energy Technology Data Exchange (ETDEWEB)
Mort Webster
2005-10-17
The determination of climate policy is a decision under uncertainty. The uncertainty in future climate change impacts is large, as is the uncertainty in the costs of potential policies. Rational and economically efficient policy choices will therefore seek to balance the expected marginal costs with the expected marginal benefits. This approach requires that the risks of future climate change be assessed. The decision process need not be formal or quantitative for descriptions of the risks to be useful. Whatever the decision procedure, a useful starting point is to have as accurate a description of climate risks as possible. Given the goal of describing uncertainty in future climate change, we need to characterize the uncertainty in the main causes of uncertainty in climate impacts. One of the major drivers of uncertainty in future climate change is the uncertainty in future emissions, both of greenhouse gases and other radiatively important species such as sulfur dioxide. In turn, the drivers of uncertainty in emissions are uncertainties in the determinants of the rate of economic growth and in the technologies of production and how those technologies will change over time. This project uses historical experience and observations from a large number of countries to construct statistical descriptions of variability and correlation in labor productivity growth and in AEEI. The observed variability then provides a basis for constructing probability distributions for these drivers. The variance of uncertainty in growth rates can be further modified by expert judgment if it is believed that future variability will differ from the past. But often, expert judgment is more readily applied to projected median or expected paths through time. Analysis of past variance and covariance provides initial assumptions about future uncertainty for quantities that are less intuitive and difficult for experts to estimate, and these variances can be normalized and then applied to mean
Uncertainties in Atomic Data and Their Propagation Through Spectral Models. I.
Bautista, M. A.; Fivet, V.; Quinet, P.; Dunn, J.; Gull, T. R.; Kallman, T. R.; Mendoza, C.
2013-01-01
We present a method for computing uncertainties in spectral models, i.e., level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data.We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of Oiii and Fe ii and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe ii]. Key words: atomic data - atomic processes - line: formation - methods: data analysis - molecular data - molecular processes - techniques: spectroscopic
Uncertainties in Atomic Data and Their Propagation Through Spectral Models. I
Bautista, Manuel A; Quinet, Pascal; Dunn, Jay; Kallman, Theodore R Gull Timothy R; Mendoza, Claudio
2013-01-01
We present a method for computing uncertainties in spectral models, i.e. level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data. We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of O III and Fe II and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe II].
Relational uncertainty in service dyads
DEFF Research Database (Denmark)
Kreye, Melanie
2016-01-01
in service dyads and how they resolve it through suitable organisational responses to increase the level of service quality. Design/methodology/approach: We apply the overall logic of Organisational Information-Processing Theory (OIPT) and present empirical insights from two industrial case studies collected...... via semi-structured interviews and secondary data. Findings: The findings suggest that relational uncertainty is caused by the partner’s unresolved organisational uncertainty, i.e. their lacking capabilities to deliver or receive (parts of) the service. Furthermore, we found that resolving...... the relational uncertainty increased the functional quality while resolving the partner’s organisational uncertainty increased the technical quality of the delivered service. Originality: We make two contributions. First, we introduce relational uncertainty to the OM literature as the inability to predict...
Uncertainty relation in Schwarzschild spacetime
Directory of Open Access Journals (Sweden)
Jun Feng
2015-04-01
Full Text Available We explore the entropic uncertainty relation in the curved background outside a Schwarzschild black hole, and find that Hawking radiation introduces a nontrivial modification on the uncertainty bound for particular observer, therefore it could be witnessed by proper uncertainty game experimentally. We first investigate an uncertainty game between a free falling observer and his static partner holding a quantum memory initially entangled with the quantum system to be measured. Due to the information loss from Hawking decoherence, we find an inevitable increase of the uncertainty on the outcome of measurements in the view of static observer, which is dependent on the mass of the black hole, the distance of observer from event horizon, and the mode frequency of quantum memory. To illustrate the generality of this paradigm, we relate the entropic uncertainty bound with other uncertainty probe, e.g., time–energy uncertainty. In an alternative game between two static players, we show that quantum information of qubit can be transferred to quantum memory through a bath of fluctuating quantum fields outside the black hole. For a particular choice of initial state, we show that the Hawking decoherence cannot counteract entanglement generation after the dynamical evolution of system, which triggers an effectively reduced uncertainty bound that violates the intrinsic limit −log2c. Numerically estimation for a proper choice of initial state shows that our result is comparable with possible real experiments. Finally, a discussion on the black hole firewall paradox in the context of entropic uncertainty relation is given.
Understanding and reducing statistical uncertainties in nebular abundance determinations
Wesson, R.; Stock, D. J.; Scicluna, P.
2012-06-01
Whenever observations are compared to theories, an estimate of the uncertainties associated with the observations is vital if the comparison is to be meaningful. However, many or even most determinations of temperatures, densities and abundances in photoionized nebulae do not quote the associated uncertainty. Those that do typically propagate the uncertainties using analytical techniques which rely on assumptions that generally do not hold. Motivated by this issue, we have developed Nebular Empirical Analysis Tool (NEAT), a new code for calculating chemical abundances in photoionized nebulae. The code carries out a standard analysis of lists of emission lines using long-established techniques to estimate the amount of interstellar extinction, calculate representative temperatures and densities, compute ionic abundances from both collisionally excited lines and recombination lines, and finally to estimate total elemental abundances using an ionization correction scheme. NEATuses a Monte Carlo technique to robustly propagate uncertainties from line flux measurements through to the derived abundances. We show that, for typical observational data, this approach is superior to analytic estimates of uncertainties. NEAT also accounts for the effect of upward biasing on measurements of lines with low signal-to-noise ratio, allowing us to accurately quantify the effect of this bias on abundance determinations. We find not only that the effect can result in significant overestimates of heavy element abundances derived from weak lines, but also that taking it into account reduces the uncertainty of these abundance determinations. Finally, we investigate the effect of possible uncertainties in R, the ratio of selective-to-total extinction, on abundance determinations. We find that the uncertainty due to this parameter is negligible compared to the statistical uncertainties due to typical line flux measurement uncertainties.
Development of a Prototype Model-Form Uncertainty Knowledge Base
Green, Lawrence L.
2016-01-01
Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.
Uncertainties in Nuclear Proliferation Modeling
Energy Technology Data Exchange (ETDEWEB)
Kim, Chul Min; Yim, Man-Sung; Park, Hyeon Seok [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)
2015-05-15
There have been various efforts in the research community to understand the determinants of nuclear proliferation and develop quantitative tools to predict nuclear proliferation events. Such systematic approaches have shown the possibility to provide warning for the international community to prevent nuclear proliferation activities. However, there are still large debates for the robustness of the actual effect of determinants and projection results. Some studies have shown that several factors can cause uncertainties in previous quantitative nuclear proliferation modeling works. This paper analyzes the uncertainties in the past approaches and suggests future works in the view of proliferation history, analysis methods, and variable selection. The research community still lacks the knowledge for the source of uncertainty in current models. Fundamental problems in modeling will remain even other advanced modeling method is developed. Before starting to develop fancy model based on the time dependent proliferation determinants' hypothesis, using graph theory, etc., it is important to analyze the uncertainty of current model to solve the fundamental problems of nuclear proliferation modeling. The uncertainty from different proliferation history coding is small. Serious problems are from limited analysis methods and correlation among the variables. Problems in regression analysis and survival analysis cause huge uncertainties when using the same dataset, which decreases the robustness of the result. Inaccurate variables for nuclear proliferation also increase the uncertainty. To overcome these problems, further quantitative research should focus on analyzing the knowledge suggested on the qualitative nuclear proliferation studies.
Measurement uncertainty of lactase-containing tablets analyzed with FTIR.
Paakkunainen, Maaret; Kohonen, Jarno; Reinikainen, Satu-Pia
2014-01-01
Uncertainty is one of the most critical aspects in determination of measurement reliability. In order to ensure accurate measurements, results need to be traceable and uncertainty measurable. In this study, homogeneity of FTIR samples is determined with a combination of variographic and multivariate approach. An approach for estimation of uncertainty within individual sample, as well as, within repeated samples is introduced. FTIR samples containing two commercial pharmaceutical lactase products (LactaNON and Lactrase) are applied as an example of the procedure. The results showed that the approach is suitable for the purpose. The sample pellets were quite homogeneous, since the total uncertainty of each pellet varied between 1.5% and 2.5%. The heterogeneity within a tablet strip was found to be dominant, as 15-20 tablets has to be analyzed in order to achieve <5.0% expanded uncertainty level. Uncertainty arising from the FTIR instrument was <1.0%. The uncertainty estimates are computed directly from FTIR spectra without any concentration information of the analyte.
Uncertainty in Regional Air Quality Modeling
Digar, Antara
Effective pollution mitigation is the key to successful air quality management. Although states invest millions of dollars to predict future air quality, the regulatory modeling and analysis process to inform pollution control strategy remains uncertain. Traditionally deterministic ‘bright-line’ tests are applied to evaluate the sufficiency of a control strategy to attain an air quality standard. A critical part of regulatory attainment demonstration is the prediction of future pollutant levels using photochemical air quality models. However, because models are uncertain, they yield a false sense of precision that pollutant response to emission controls is perfectly known and may eventually mislead the selection of control policies. These uncertainties in turn affect the health impact assessment of air pollution control strategies. This thesis explores beyond the conventional practice of deterministic attainment demonstration and presents novel approaches to yield probabilistic representations of pollutant response to emission controls by accounting for uncertainties in regional air quality planning. Computationally-efficient methods are developed and validated to characterize uncertainty in the prediction of secondary pollutant (ozone and particulate matter) sensitivities to precursor emissions in the presence of uncertainties in model assumptions and input parameters. We also introduce impact factors that enable identification of model inputs and scenarios that strongly influence pollutant concentrations and sensitivity to precursor emissions. We demonstrate how these probabilistic approaches could be applied to determine the likelihood that any control measure will yield regulatory attainment, or could be extended to evaluate probabilistic health benefits of emission controls, considering uncertainties in both air quality models and epidemiological concentration-response relationships. Finally, ground-level observations for pollutant (ozone) and precursor
Optimal uncertainty relations in a modified Heisenberg algebra
Abdelkhalek, Kais; Fiedler, Leander; Mangano, Gianpiero; Schwonnek, René
2016-01-01
Various theories that aim at unifying gravity with quantum mechanics suggest modifications of the Heisenberg algebra for position and momentum. From the perspective of quantum mechanics, such modifications lead to new uncertainty relations which are thought (but not proven) to imply the existence of a minimal observable length. Here we prove this statement in a framework of sufficient physical and structural assumptions. Moreover, we present a general method that allows to formulate optimal and state-independent variance-based uncertainty relations. In addition, instead of variances, we make use of entropies as a measure of uncertainty and provide uncertainty relations in terms of min- and Shannon entropies. We compute the corresponding entropic minimal lengths and find that the minimal length in terms of min-entropy is exactly one bit.
Vector network analyzer (VNA) measurements and uncertainty assessment
Shoaib, Nosherwan
2017-01-01
This book describes vector network analyzer measurements and uncertainty assessments, particularly in waveguide test-set environments, in order to establish their compatibility to the International System of Units (SI) for accurate and reliable characterization of communication networks. It proposes a fully analytical approach to measurement uncertainty evaluation, while also highlighting the interaction and the linear propagation of different uncertainty sources to compute the final uncertainties associated with the measurements. The book subsequently discusses the dimensional characterization of waveguide standards and the quality of the vector network analyzer (VNA) calibration techniques. The book concludes with an in-depth description of the novel verification artefacts used to assess the performance of the VNAs. It offers a comprehensive reference guide for beginners to experts, in both academia and industry, whose work involves the field of network analysis, instrumentation and measurements.
Optimal uncertainty relations in a modified Heisenberg algebra
Abdelkhalek, Kais; Chemissany, Wissam; Fiedler, Leander; Mangano, Gianpiero; Schwonnek, René
2016-12-01
Various theories that aim at unifying gravity with quantum mechanics suggest modifications of the Heisenberg algebra for position and momentum. From the perspective of quantum mechanics, such modifications lead to new uncertainty relations that are thought (but not proven) to imply the existence of a minimal observable length. Here we prove this statement in a framework of sufficient physical and structural assumptions. Moreover, we present a general method that allows us to formulate optimal and state-independent variance-based uncertainty relations. In addition, instead of variances, we make use of entropies as a measure of uncertainty and provide uncertainty relations in terms of min and Shannon entropies. We compute the corresponding entropic minimal lengths and find that the minimal length in terms of min entropy is exactly 1 bit.
Uncertainty principle in larmor clock
Institute of Scientific and Technical Information of China (English)
QIAO Chuan; REN Zhong-Zhou
2011-01-01
It is well known that the spin operators of a quantum particle must obey uncertainty relations.We use the uncertainty principle to study the Larmor clock.To avoid breaking the uncertainty principle,Larmor time can be defined as the ratio of the phase difference between a spin-up particle and a spin-down particle to the corresponding Larmor frequency.The connection between the dwell time and the Larmor time has also been confirmed.Moreover,the results show that the behavior of the Larmor time depends on the height and width of the barrier.
Realising the Uncertainty Enabled Model Web
Cornford, D.; Bastin, L.; Pebesma, E. J.; Williams, M.; Stasch, C.; Jones, R.; Gerharz, L.
2012-12-01
conversion between uncertainty types, and between the spatial / temporal support of service inputs / outputs. Finally we describe the tools being generated within the UncertWeb project, considering three main aspects: i) Elicitation of uncertainties on model inputs. We are developing tools to enable domain experts to provide judgements about input uncertainties from UncertWeb model components (e.g. parameters in meteorological models) which allow panels of experts to engage in the process and reach a consensus view on the current knowledge / beliefs about that parameter or variable. We are developing systems for continuous and categorical variables as well as stationary spatial fields. ii) Visualisation of the resulting uncertain outputs from the end of the workflow, but also at intermediate steps. At this point we have prototype implementations driven by the requirements from the use cases that motivate UncertWeb. iii) Sensitivity and uncertainty analysis on model outputs. Here we show the design of the overall system we are developing, including the deployment of an emulator framework to allow computationally efficient approaches. We conclude with a summary of the open issues and remaining challenges we are facing in UncertWeb, and provide a brief overview of how we plan to tackle these.
Generalized uncertainty principle and black hole thermodynamics
Gangopadhyay, Sunandan; Saha, Anirban
2013-01-01
We study the Schwarzschild and Reissner-Nordstr\\"{o}m black hole thermodynamics using the simplest form of the generalized uncertainty principle (GUP) proposed in the literature. The expressions for the mass-temperature relation, heat capacity and entropy are obtained in both cases from which the critical and remnant masses are computed. Our results are exact and reveal that these masses are identical and larger than the so called singular mass for which the thermodynamics quantities become ill-defined. The expression for the entropy reveals the well known area theorem in terms of the horizon area in both cases upto leading order corrections from GUP. The area theorem written in terms of a new variable which can be interpreted as the reduced horizon area arises only when the computation is carried out to the next higher order correction from GUP.
Non-scalar uncertainty: Uncertainty in dynamic systems
Martinez, Salvador Gutierrez
1992-01-01
The following point is stated throughout the paper: dynamic systems are usually subject to uncertainty, be it the unavoidable quantic uncertainty when working with sufficiently small scales or when working in large scales uncertainty can be allowed by the researcher in order to simplify the problem, or it can be introduced by nonlinear interactions. Even though non-quantic uncertainty can generally be dealt with by using the ordinary probability formalisms, it can also be studied with the proposed non-scalar formalism. Thus, non-scalar uncertainty is a more general theoretical framework giving insight into the nature of uncertainty and providing a practical tool in those cases in which scalar uncertainty is not enough, such as when studying highly nonlinear dynamic systems. This paper's specific contribution is the general concept of non-scalar uncertainty and a first proposal for a methodology. Applications should be based upon this methodology. The advantage of this approach is to provide simpler mathematical models for prediction of the system states. Present conventional tools for dealing with uncertainty prove insufficient for an effective description of some dynamic systems. The main limitations are overcome abandoning ordinary scalar algebra in the real interval (0, 1) in favor of a tensor field with a much richer structure and generality. This approach gives insight into the interpretation of Quantum Mechanics and will have its most profound consequences in the fields of elementary particle physics and nonlinear dynamic systems. Concepts like 'interfering alternatives' and 'discrete states' have an elegant explanation in this framework in terms of properties of dynamic systems such as strange attractors and chaos. The tensor formalism proves especially useful to describe the mechanics of representing dynamic systems with models that are closer to reality and have relatively much simpler solutions. It was found to be wise to get an approximate solution to an
Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
Starczewski, Janusz T
2013-01-01
This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory. In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...
GLOBALIZATION AND INDICATORS OF UNCERTAINTY
Directory of Open Access Journals (Sweden)
Matjaž Škabar
2017-01-01
Full Text Available The aim of this research is to explore the influence of culture, which understood in a broader sense, as focused towards the uncertainties in modern societies. Using theory and empirical data, we will determine the logic and dynamics of changes and the attitude towards changes and uncertainties in modern societies. This article, based on a theoretical thesis, empirically tests the correlation between the index of globalization and different indicators of individualism, as well as the attitude towards uncertainty and reflectiveness. We managed to prove the correlation between the globalization index and indicators of individualism. However, when we look at the links between the indicators of uncertainty and reflexivity, the correlations prove to be complex and inconsistent.
Climate Projections and Uncertainty Communication.
Joslyn, Susan L; LeClerc, Jared E
2016-01-01
Lingering skepticism about climate change might be due in part to the way climate projections are perceived by members of the public. Variability between scientists' estimates might give the impression that scientists disagree about the fact of climate change rather than about details concerning the extent or timing. Providing uncertainty estimates might clarify that the variability is due in part to quantifiable uncertainty inherent in the prediction process, thereby increasing people's trust in climate projections. This hypothesis was tested in two experiments. Results suggest that including uncertainty estimates along with climate projections leads to an increase in participants' trust in the information. Analyses explored the roles of time, place, demographic differences (e.g., age, gender, education level, political party affiliation), and initial belief in climate change. Implications are discussed in terms of the potential benefit of adding uncertainty estimates to public climate projections.
Numerical modeling of economic uncertainty
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans
2007-01-01
Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...
Thermodynamic and relativistic uncertainty relations
Artamonov, A. A.; Plotnikov, E. M.
2017-01-01
Thermodynamic uncertainty relation (UR) was verified experimentally. The experiments have shown the validity of the quantum analogue of the zeroth law of stochastic thermodynamics in the form of the saturated Schrödinger UR. We have also proposed a new type of UR for the relativistic mechanics. These relations allow us to consider macroscopic phenomena within the limits of the ratio of the uncertainty relations for different physical quantities.
Computational movement analysis
Laube, Patrick
2014-01-01
This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fi
Uncertainties in land use data
Directory of Open Access Journals (Sweden)
G. Castilla
2007-11-01
Full Text Available This paper deals with the description and assessment of uncertainties in land use data derived from Remote Sensing observations, in the context of hydrological studies. Land use is a categorical regionalised variable reporting the main socio-economic role each location has, where the role is inferred from the pattern of occupation of land. The properties of this pattern that are relevant to hydrological processes have to be known with some accuracy in order to obtain reliable results; hence, uncertainty in land use data may lead to uncertainty in model predictions. There are two main uncertainties surrounding land use data, positional and categorical. The first one is briefly addressed and the second one is explored in more depth, including the factors that influence it. We (1 argue that the conventional method used to assess categorical uncertainty, the confusion matrix, is insufficient to propagate uncertainty through distributed hydrologic models; (2 report some alternative methods to tackle this and other insufficiencies; (3 stress the role of metadata as a more reliable means to assess the degree of distrust with which these data should be used; and (4 suggest some practical recommendations.
Wildfire Decision Making Under Uncertainty
Thompson, M.
2013-12-01
Decisions relating to wildfire management are subject to multiple sources of uncertainty, and are made by a broad range of individuals, across a multitude of environmental and socioeconomic contexts. In this presentation I will review progress towards identification and characterization of uncertainties and how this information can support wildfire decision-making. First, I will review a typology of uncertainties common to wildfire management, highlighting some of the more salient sources of uncertainty and how they present challenges to assessing wildfire risk. This discussion will cover the expanding role of burn probability modeling, approaches for characterizing fire effects, and the role of multi-criteria decision analysis, and will provide illustrative examples of integrated wildfire risk assessment across a variety of planning scales. Second, I will describe a related uncertainty typology that focuses on the human dimensions of wildfire management, specifically addressing how social, psychological, and institutional factors may impair cost-effective risk mitigation. This discussion will encompass decision processes before, during, and after fire events, with a specific focus on active management of complex wildfire incidents. An improved ability to characterize uncertainties faced in wildfire management could lead to improved delivery of decision support, targeted communication strategies, and ultimately to improved wildfire management outcomes.
Formal modeling of a system of chemical reactions under uncertainty.
Ghosh, Krishnendu; Schlipf, John
2014-10-01
We describe a novel formalism representing a system of chemical reactions, with imprecise rates of reactions and concentrations of chemicals, and describe a model reduction method, pruning, based on the chemical properties. We present two algorithms, midpoint approximation and interval approximation, for construction of efficient model abstractions with uncertainty in data. We evaluate computational feasibility by posing queries in computation tree logic (CTL) on a prototype of extracellular-signal-regulated kinase (ERK) pathway.
Bounds in the generalized Weber problem under locational uncertainty
DEFF Research Database (Denmark)
Juel, Henrik
1981-01-01
An existing analysis of the bounds on the Weber problem solution under uncertainty is incorrect. For the generalized problem with arbitrary measures of distance, we give easily computable ranges on the bounds and state the conditions under which the exact values of the bounds can be found...
Pseudoharmonic oscillator in quantum mechanics with a generalized uncertainty principle
Boukhellout, Abdelmalek
2013-01-01
The pseudoharmonic oscillator potential is studied in quantum mechanics with a generalized uncertainty relation characterized by the existence of a minimal length. By using the perturbative approach of Brau, we compute the correction to the energy spectrum in the first order of the minimal length parameter {\\beta}. The effect of the minimal length on the vibration-rotation of diatomic molecules is discussed.
Quantification of Modelling Uncertainties in Turbulent Flow Simulations
Edeling, W.N.
2015-01-01
The goal of this thesis is to make predictive simulations with Reynolds-Averaged Navier-Stokes (RANS) turbulence models, i.e. simulations with a systematic treatment of model and data uncertainties and their propagation through a computational model to produce predictions of quantities of interest w
Campolina, Daniel de A. M.; Lima, Claubia P. B.; Veloso, Maria Auxiliadora F.
2014-06-01
For all the physical components that comprise a nuclear system there is an uncertainty. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a best estimate calculation that has been replacing the conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using a Monte Carlo code by sampling the input parameters is recent because of the huge computational effort required. In this work a sample space of MCNPX calculations was used to propagate the uncertainty. The sample size was optimized using the Wilks formula for a 95th percentile and a two-sided statistical tolerance interval of 95%. Uncertainties in input parameters of the reactor considered included geometry dimensions and densities. It was showed the capacity of the sampling-based method for burnup when the calculations sample size is optimized and many parameter uncertainties are investigated together, in the same input.
Power system transient stability simulation under uncertainty based on Taylor model arithmetic
Institute of Scientific and Technical Information of China (English)
Shouxiang WANG; Zhijie ZHENG; Chengshan WANG
2009-01-01
The Taylor model arithmetic is introduced to deal with uncertainty. The uncertainty of model parameters is described by Taylor models and each variable in functions is replaced with the Taylor model (TM). Thus,time domain simulation under uncertainty is transformed to the integration of TM-based differential equations. In this paper, the Taylor series method is employed to compute differential equations; moreover, power system time domain simulation under uncertainty based on Taylor model method is presented. This method allows a rigorous estimation of the influence of either form of uncertainty and only needs one simulation. It is computationally fast compared with the Monte Carlo method, which is another technique for uncertainty analysis. The proposed method has been tested on the 39-bus New England system. The test results illustrate the effectiveness and practical value of the approach by comparing with the results of Monte Carlo simulation and traditional time domain simulation.
Fazzari, D M
2001-01-01
This report presents the results of an evaluation of the Total Measurement Uncertainty (TMU) for the Canberra manufactured Segmented Gamma Scanner Assay System (SGSAS) as employed at the Hanford Plutonium Finishing Plant (PFP). In this document, TMU embodies the combined uncertainties due to all of the individual random and systematic sources of measurement uncertainty. It includes uncertainties arising from corrections and factors applied to the analysis of transuranic waste to compensate for inhomogeneities and interferences from the waste matrix and radioactive components. These include uncertainty components for any assumptions contained in the calibration of the system or computation of the data. Uncertainties are propagated at 1 sigma. The final total measurement uncertainty value is reported at the 95% confidence level. The SGSAS is a gamma assay system that is used to assay plutonium and uranium waste. The SGSAS system can be used in a stand-alone mode to perform the NDA characterization of a containe...
A discussion on the Heisenberg uncertainty principle from the perspective of special relativity
Nanni, Luca
2016-09-01
In this note, we consider the implications of the Heisenberg uncertainty principle (HUP) when computing uncertainties that affect the main dynamical quantities, from the perspective of special relativity. Using the well-known formula for propagating statistical errors, we prove that the uncertainty relations between the moduli of conjugate observables are not relativistically invariant. The new relationships show that, in experiments involving relativistic particles, limitations of the precision of a quantity obtained by indirect calculations may affect the final result.
Capturing and Displaying Uncertainty in the Common Tactical/Environmental Picture
2016-06-07
multistatic active detection, and incorporated this characterization into a Bayesian track-before-detect system called, the Likelihood Ratio Tracker (LRT...for modeling and computing the distribution of the uncertainty in Signal Excess (SE) prediction for multistatic active detection of submarines...resulting from uncertainty in environmental predictions, and (2) to develop methods for accounting for this uncertainty in a Likelihood Ratio Tracker (LRT
Sustainable energy development under uncertainty
Energy Technology Data Exchange (ETDEWEB)
Fuss, S.
2008-04-24
This thesis has contributed to investment decision-making under uncertainty and irreversibility with particular focus on the transition towards a more sustainable mix of electricity-generating technologies. The models introduced here do thus not only provide important insights for investors, but also for policy makers interested in curtailing greenhouse gas (GHG) emissions for the sake of a deceleration of global warming, who therefore need to understand how investors respond to uncertainties, climate change policy and other factors. Part 1 is partly devoted to frameworks based on principles from real options theory. Chapter 3 presents a model, in which we deal with different types of uncertainty afiecting the investor. In liberalized electricity markets, investors nowadays do not only face uncertainty from volatile electricity prices, but also from the possibility of stricter climate change policy. We investigate this in a real options framework with two types of power plants (both coal-fired, but one with a carbon capture and storage module, which therefore emits less CO2), where the prices of electricity and CO2 emissions are stochastic. In particular, we analyze the response of long-term investment to (higher) uncertainty about CO2 prices, which can come from two sources: price fluctuations around an average, rising price that might as well be market-driven, and uncertainty about the actions of the government, which can lead to sudden price jumps or drops. We find that producers facing market uncertainty optimize under incomplete information and invest into the carbon-saving technology earlier than they would have done if they had known what the prices indeed are, while policy uncertainty leads to postponement of investment, as the option value of waiting for the revelation of the policy outcome more than outweighs the losses associated with ongoing, continuously rising CO2 costs. Chapter 4 is about a real options model, which considers both fuel price risk and
Critical loads - assessment of uncertainty
Energy Technology Data Exchange (ETDEWEB)
Barkman, A.
1998-10-01
The effects of data uncertainty in applications of the critical loads concept were investigated on different spatial resolutions in Sweden and northern Czech Republic. Critical loads of acidity (CL) were calculated for Sweden using the biogeochemical model PROFILE. Three methods with different structural complexity were used to estimate the adverse effects of S0{sub 2} concentrations in northern Czech Republic. Data uncertainties in the calculated critical loads/levels and exceedances (EX) were assessed using Monte Carlo simulations. Uncertainties within cumulative distribution functions (CDF) were aggregated by accounting for the overlap between site specific confidence intervals. Aggregation of data uncertainties within CDFs resulted in lower CL and higher EX best estimates in comparison with percentiles represented by individual sites. Data uncertainties were consequently found to advocate larger deposition reductions to achieve non-exceedance based on low critical loads estimates on 150 x 150 km resolution. Input data were found to impair the level of differentiation between geographical units at all investigated resolutions. Aggregation of data uncertainty within CDFs involved more constrained confidence intervals for a given percentile. Differentiation as well as identification of grid cells on 150 x 150 km resolution subjected to EX was generally improved. Calculation of the probability of EX was shown to preserve the possibility to differentiate between geographical units. Re-aggregation of the 95%-ile EX on 50 x 50 km resolution generally increased the confidence interval for each percentile. Significant relationships were found between forest decline and the three methods addressing risks induced by S0{sub 2} concentrations. Modifying S0{sub 2} concentrations by accounting for the length of the vegetation period was found to constitute the most useful trade-off between structural complexity, data availability and effects of data uncertainty. Data
Uncertainty Estimation Improves Energy Measurement and Verification Procedures
Energy Technology Data Exchange (ETDEWEB)
Walter, Travis; Price, Phillip N.; Sohn, Michael D.
2014-05-14
Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.
Users manual for the FORSS sensitivity and uncertainty analysis code system
Energy Technology Data Exchange (ETDEWEB)
Lucius, J.L.; Weisbin, C.R.; Marable, J.H.; Drischler, J.D.; Wright, R.Q.; White, J.E.
1981-01-01
FORSS is a code system used to study relationships between nuclear reaction cross sections, integral experiments, reactor performance parameter predictions and associated uncertainties. This report describes the computing environment and the modules currently used to implement FORSS Sensitivity and Uncertainty Methodology.
Incorporating Uncertainty into Spacecraft Mission and Trajectory Design
Juliana D., Feldhacker
The complex nature of many astrodynamic systems often leads to high computational costs or degraded accuracy in the analysis and design of spacecraft missions, and the incorporation of uncertainty into the trajectory optimization process often becomes intractable. This research applies mathematical modeling techniques to reduce computational cost and improve tractability for design, optimization, uncertainty quantication (UQ) and sensitivity analysis (SA) in astrodynamic systems and develops a method for trajectory optimization under uncertainty (OUU). This thesis demonstrates the use of surrogate regression models and polynomial chaos expansions for the purpose of design and UQ in the complex three-body system. Results are presented for the application of the models to the design of mid-eld rendezvous maneuvers for spacecraft in three-body orbits. The models are shown to provide high accuracy with no a priori knowledge on the sample size required for convergence. Additionally, a method is developed for the direct incorporation of system uncertainties into the design process for the purpose of OUU and robust design; these methods are also applied to the rendezvous problem. It is shown that the models can be used for constrained optimization with orders of magnitude fewer samples than is required for a Monte Carlo approach to the same problem. Finally, this research considers an application for which regression models are not well-suited, namely UQ for the kinetic de ection of potentially hazardous asteroids under the assumptions of real asteroid shape models and uncertainties in the impact trajectory and the surface material properties of the asteroid, which produce a non-smooth system response. An alternate set of models is presented that enables analytic computation of the uncertainties in the imparted momentum from impact. Use of these models for a survey of asteroids allows conclusions to be drawn on the eects of an asteroid's shape on the ability to
Word learning under infinite uncertainty
Blythe, Richard A; Smith, Kenny
2014-01-01
Language learners learn the meanings of many thousands of words, despite encountering them in complex environments where infinitely many meanings might be inferred by the learner as their true meaning. This problem of infinite referential uncertainty is often attributed to Willard Van Orman Quine. We provide a mathematical formalisation of an ideal cross-situational learner attempting to learn under infinite referential uncertainty, and identify conditions under which this can happen. As Quine's intuitions suggest, learning under infinite uncertainty is possible, provided that learners have some means of ranking candidate word meanings in terms of their plausibility; furthermore, our analysis shows that this ranking could in fact be exceedingly weak, implying that constraints allowing learners to infer the plausibility of candidate word meanings could also be weak.
Uncertainty and Sensitivity Analyses Plan
Energy Technology Data Exchange (ETDEWEB)
Simpson, J.C.; Ramsdell, J.V. Jr.
1993-04-01
Hanford Environmental Dose Reconstruction (HEDR) Project staff are developing mathematical models to be used to estimate the radiation dose that individuals may have received as a result of emissions since 1944 from the US Department of Energy's (DOE) Hanford Site near Richland, Washington. An uncertainty and sensitivity analyses plan is essential to understand and interpret the predictions from these mathematical models. This is especially true in the case of the HEDR models where the values of many parameters are unknown. This plan gives a thorough documentation of the uncertainty and hierarchical sensitivity analysis methods recommended for use on all HEDR mathematical models. The documentation includes both technical definitions and examples. In addition, an extensive demonstration of the uncertainty and sensitivity analysis process is provided using actual results from the Hanford Environmental Dose Reconstruction Integrated Codes (HEDRIC). This demonstration shows how the approaches used in the recommended plan can be adapted for all dose predictions in the HEDR Project.
Energy Technology Data Exchange (ETDEWEB)
Davis, C B
1987-08-01
The uncertainties of calculations of loss-of-feedwater transients at Davis-Besse Unit 1 were determined to address concerns of the US Nuclear Regulatory Commission relative to the effectiveness of feed and bleed cooling. Davis-Besse Unit 1 is a pressurized water reactor of the raised-loop Babcock and Wilcox design. A detailed, quality-assured RELAP5/MOD2 model of Davis-Besse was developed at the Idaho National Engineering Laboratory. The model was used to perform an analysis of the loss-of-feedwater transient that occurred at Davis-Besse on June 9, 1985. A loss-of-feedwater transient followed by feed and bleed cooling was also calculated. The evaluation of uncertainty was based on the comparisons of calculations and data, comparisons of different calculations of the same transient, sensitivity calculations, and the propagation of the estimated uncertainty in initial and boundary conditions to the final calculated results.
Sankararaman, Shankar
2016-01-01
This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.
Survey and Evaluate Uncertainty Quantification Methodologies
Energy Technology Data Exchange (ETDEWEB)
Lin, Guang; Engel, David W.; Eslinger, Paul W.
2012-02-01
The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon
Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies
Energy Technology Data Exchange (ETDEWEB)
Safta, Cosmin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Najm, Habib N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Phipps, Eric Todd [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-09-01
Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.
Uncertainty Analysis in Population-Based Disease Microsimulation Models
Directory of Open Access Journals (Sweden)
Behnam Sharif
2012-01-01
Full Text Available Objective. Uncertainty analysis (UA is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models.
Linear Programming Problems for Generalized Uncertainty
Thipwiwatpotjana, Phantipa
2010-01-01
Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…
Uncertainty quantification in lattice QCD calculations for nuclear physics
Energy Technology Data Exchange (ETDEWEB)
Beane, Silas R. [Univ. of Washington, Seattle, WA (United States); Detmold, William [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Orginos, Kostas [College of William and Mary, Williamsburg, VA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Savage, Martin J. [Institute for Nuclear Theory, Seattle, WA (United States)
2015-02-05
The numerical technique of Lattice QCD holds the promise of connecting the nuclear forces, nuclei, the spectrum and structure of hadrons, and the properties of matter under extreme conditions with the underlying theory of the strong interactions, quantum chromodynamics. A distinguishing, and thus far unique, feature of this formulation is that all of the associated uncertainties, both statistical and systematic can, in principle, be systematically reduced to any desired precision with sufficient computational and human resources. As a result, we review the sources of uncertainty inherent in Lattice QCD calculations for nuclear physics, and discuss how each is quantified in current efforts.
Large-Scale Inverse Problems and Quantification of Uncertainty
Biegler, Lorenz; Ghattas, Omar
2010-01-01
Large-scale inverse problems and associated uncertainty quantification has become an important area of research, central to a wide range of science and engineering applications. Written by leading experts in the field, Large-scale Inverse Problems and Quantification of Uncertainty focuses on the computational methods used to analyze and simulate inverse problems. The text provides PhD students, researchers, advanced undergraduate students, and engineering practitioners with the perspectives of researchers in areas of inverse problems and data assimilation, ranging from statistics and large-sca
Adaptive second-order sliding mode control with uncertainty compensation
Bartolini, G.; Levant, A.; Pisano, A.; Usai, E.
2016-09-01
This paper endows the second-order sliding mode control (2-SMC) approach with additional capabilities of learning and control adaptation. We present a 2-SMC scheme that estimates and compensates for the uncertainties affecting the system dynamics. It also adjusts the discontinuous control effort online, so that it can be reduced to arbitrarily small values. The proposed scheme is particularly useful when the available information regarding the uncertainties is conservative, and the classical `fixed-gain' SMC would inevitably lead to largely oversized discontinuous control effort. Benefits from the viewpoint of chattering reduction are obtained, as confirmed by computer simulations.
Uncertainty Modeling Based on Bayesian Network in Ontology Mapping
Institute of Scientific and Technical Information of China (English)
LI Yuhua; LIU Tao; SUN Xiaolin
2006-01-01
How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks (BN). In our approach, ontology Web language (OWL) is extended to add probabilistic markups for attaching probability information, the source and target ontologies (expressed by patulous OWL) are translated into bayesian networks (BNs), the mapping between the two ontologies can be digged out by constructing the conditional probability tables (CPTs) of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure (IPFP). The basic idea of this framework and algorithm are validated by positive results from computer experiments.
Controlling Uncertainty Decision Making and Learning in Complex Worlds
Osman, Magda
2010-01-01
Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us.: Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering; Presents a new framework that is designed to integrate a variety of disparate fields of research; Represents the first book of its kind to provide a general overview of work related to understanding control
Statistics, Uncertainty, and Transmitted Variation
Energy Technology Data Exchange (ETDEWEB)
Wendelberger, Joanne Roth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-11-05
The field of Statistics provides methods for modeling and understanding data and making decisions in the presence of uncertainty. When examining response functions, variation present in the input variables will be transmitted via the response function to the output variables. This phenomenon can potentially have significant impacts on the uncertainty associated with results from subsequent analysis. This presentation will examine the concept of transmitted variation, its impact on designed experiments, and a method for identifying and estimating sources of transmitted variation in certain settings.
Regulating renewable resources under uncertainty
DEFF Research Database (Denmark)
Hansen, Lars Gårn
Renewable natural resources (like water, fish and wildlife stocks, forests and grazing lands) are critical for the livelihood of millions of people and understanding how they can be managed efficiently is an important economic problem. I show how regulator uncertainty about different economic......) that a pro-quota result under uncertainty about prices and marginal costs is unlikely, requiring that the resource growth function is highly concave locally around the optimum and, 3) that quotas are always preferred if uncertainly about underlying structural economic parameters dominates. These results...
Uncertainties in offsite consequence analysis
Energy Technology Data Exchange (ETDEWEB)
Young, M.L.; Harper, F.T.; Lui, C.H.
1996-03-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequences from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the U.S. Nuclear Regulatory Commission and the European Commission began co-sponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables using a formal expert judgment elicitation and evaluation process. This paper focuses on the methods used in and results of this on-going joint effort.
Review on Generalized Uncertainty Principle
Tawfik, Abdel Nasser
2015-01-01
Based on string theory, black hole physics, doubly special relativity and some "thought" experiments, minimal distance and/or maximum momentum are proposed. As alternatives to the generalized uncertainty principle (GUP), the modified dispersion relation, the space noncommutativity, the Lorentz invariance violation, and the quantum-gravity-induced birefringence effects are summarized. The origin of minimal measurable quantities and the different GUP approaches are reviewed and the corresponding observations are analysed. Bounds on the GUP parameter are discussed and implemented in understanding recent PLANCK observations on the cosmic inflation. The higher-order GUP approaches predict minimal length uncertainty with and without maximum momenta.
Systemic change increases model projection uncertainty
Verstegen, Judith; Karssenberg, Derek; van der Hilst, Floor; Faaij, André
2014-05-01
the neighbourhood doubled, while the influence of slope and potential yield decreased by 75% and 25% respectively. Allowing these systemic changes to occur in our CA in the future (up to 2022) resulted in an increase in model projection uncertainty by a factor two compared to the assumption of a stationary system. This means that the assumption of a constant model structure is not adequate and largely underestimates uncertainty in the projection. References Verstegen, J.A., Karssenberg, D., van der Hilst, F., Faaij, A.P.C., 2014. Identifying a land use change cellular automaton by Bayesian data assimilation. Environmental Modelling & Software 53, 121-136. Verstegen, J.A., Karssenberg, D., van der Hilst, F., Faaij, A.P.C., 2012. Spatio-Temporal Uncertainty in Spatial Decision Support Systems: a Case Study of Changing Land Availability for Bioenergy Crops in Mozambique. Computers , Environment and Urban Systems 36, 30-42. Wald, A., Wolfowitz, J., 1940. On a test whether two samples are from the same population. The Annals of Mathematical Statistics 11, 147-162.
Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise
West, Thomas K., IV; Bretl, Katherine N.; Walker, Eric L.; Pinier, Jeremy T.
2015-01-01
The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69o DeltaWing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.
Uncertainty in Vs30-based site response
Thompson, Eric; Wald, David J.
2016-01-01
Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic‐hazard analysis usually relies on ground‐motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time‐averaged shear‐wave velocity to 30 m (VS30) and basin depth parameters. Because VS30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which VS30 has been measured to address effects of VS30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground‐motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured VS30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.
Uncertainty in Greenland glacial isostatic adjustment (Invited)
Milne, G. A.; Lecavalier, B.; Kjeldsen, K. K.; Kjaer, K.; Wolstencroft, M.; Wake, L. M.; Simpson, M. J.; Long, A. J.; Woodroffe, S.; Korsgaard, N. J.; Bjork, A. A.; Khan, S. A.
2013-12-01
It is well known that the interpretation of geodetic data in Greenland to constrain recent ice mass changes requires knowledge of isostatic land motion associated with past changes in the ice sheet. In this talk we will consider a variety of factors that limit how well the signal due to past mass changes (commonly referred to as glacial isostatic adjustment (GIA)) can be defined. Predictions based on a new model of Greenland GIA will be shown. Using these predictions as a reference, we will consider the influence of plausible variations in some key aspects of both the Earth and ice load components of the GIA model on predictions of land motion and gravity changes. The sensitivity of model output to plausible variations in both depth-dependent and lateral viscosity structure will be considered. With respect to the ice model, we will compare the relative contributions of loading during key periods of the ice history with a focus on the past few thousand years. In particular, we will show predictions of contemporary land motion and gravity changes due to loading changes following the Little Ice Age computed using a new reconstruction of ice thickness changes based largely on empirical data. A primary contribution of this work will be the identification of dominant sources of uncertainty in current models of Greenland GIA and the regions most significantly affected by this uncertainty.
Adaptive Strategies for Materials Design using Uncertainties
Balachandran, Prasanna V.; Xue, Dezhen; Theiler, James; Hogden, John; Lookman, Turab
2016-01-01
We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.
Traffic forecasts under uncertainty and capacity constraints
2009-01-01
Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This paper quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process bas...
A preliminary uncertainty analysis of phenomenological inputs in TEXAS-V code
Energy Technology Data Exchange (ETDEWEB)
Park, S. H.; Kim, H. D.; Ahn, K. I. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2010-10-15
Uncertainty analysis is important step in safety analysis of nuclear power plants. The better estimate for the computer codes is on the increase instead of conservative codes. These efforts aim to get more precise evaluation of safety margins, and aim at determining the rate of change in the prediction of codes with one or more input parameters varies within its range of interest. From this point of view, a severe accident uncertainty analysis system, SAUNA, has been improved for TEXAS-V FCI uncertainty analysis. The main objective of this paper is to present the TEXAS FCI uncertainty analysis results implemented through the SAUNA code
Corwin, Rebecca L W
2011-09-01
The idea that foods rich in fat and sugar may be addictive has generated much interest, as well as controversy, among both scientific and lay communities. Recent research indicates that fatty and sugary food in-and-of itself is not addictive. Rather, the food and the context in which it is consumed interact to produce an addiction-like state. One of the contexts that appears to be important is the intermittent opportunity to consume foods rich in fat and sugar in environments where food is plentiful. Animal research indicates that, under these conditions, intake of the fatty sugary food escalates across time and binge-type behavior develops. However, the mechanisms that account for the powerful effect of intermittency on ingestive behavior have only begun to be elucidated. In this review, it is proposed that intermittency stimulates appetitive behavior that is associated with uncertainty regarding what, when, and how much of the highly palatable food to consume. Uncertainty may stimulate consumption of optional fatty and sugary treats due to differential firing of midbrain dopamine neurons, activation of the stress axis, and involvement of orexin signaling. In short, uncertainty may produce an aversive state that bingeing on palatable food can alleviate, however temporarily. "Food addiction" may not be "addiction" to food at all; it may be a response to uncertainty within environments of food abundance.
Adressing Replication and Model Uncertainty
DEFF Research Database (Denmark)
Ebersberger, Bernd; Galia, Fabrice; Laursen, Keld
Many fields of strategic management are subject to an important degree of model uncertainty. This is because the true model, and therefore the selection of appropriate explanatory variables, is essentially unknown. Drawing on the literature on the determinants of innovation, and by analyzing inno...
Model uncertainty in growth empirics
Prüfer, P.
2008-01-01
This thesis applies so-called Bayesian model averaging (BMA) to three different economic questions substantially exposed to model uncertainty. Chapter 2 addresses a major issue of modern development economics: the analysis of the determinants of pro-poor growth (PPG), which seeks to combine high gro
Numerical modeling of economic uncertainty
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans
2007-01-01
Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...... are made between alternative modeling methods, and characteristics of the methods are discussed....
Decision making uncertainty, imperfection, deliberation and scalability
Kárný, Miroslav; Wolpert, David
2015-01-01
This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selﬁsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: • task allocation to maximize “the wisdom of the crowd”; • design of a society of “edutainment” robots who account for one anothers’ emotional states; • recognizing and counteracting seemingly non-rational human decision making; • coping with extreme scale when learning causality in networks; • efﬁciently incorporating expert knowledge in personalized...
Quantifying and reducing uncertainties in cancer therapy
Barrett, Harrison H.; Alberts, David S.; Woolfenden, James M.; Liu, Zhonglin; Caucci, Luca; Hoppin, John W.
2015-03-01
There are two basic sources of uncertainty in cancer chemotherapy: how much of the therapeutic agent reaches the cancer cells, and how effective it is in reducing or controlling the tumor when it gets there. There is also a concern about adverse effects of the therapy drug. Similarly in external-beam radiation therapy or radionuclide therapy, there are two sources of uncertainty: delivery and efficacy of the radiation absorbed dose, and again there is a concern about radiation damage to normal tissues. The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control vs. the probability of normal-tissue complications as the overall radiation dose level is varied, e.g. by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy. The TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. In this paper we discuss the potential of using mathematical models of drug delivery and tumor response with imaging data to estimate AUTOC for chemotherapy, again for a single patient. This approach provides a basis for truly personalized therapy and for rigorously assessing and optimizing the therapy regimen for the particular patient. A key role is played by Emission Computed Tomography (PET or SPECT) of radiolabeled chemotherapy drugs.
Understanding and reducing statistical uncertainties in nebular abundance determinations
Wesson, R; Scicluna, P
2012-01-01
Whenever observations are compared to theories, an estimate of the uncertainties associated with the observations is vital if the comparison is to be meaningful. However, many determinations of temperatures, densities and abundances in photoionized nebulae do not quote the associated uncertainty. Those that do typically propagate the uncertainties using analytical techniques which rely on assumptions that generally do not hold. Motivated by this issue, we have developed NEAT (Nebular Empirical Analysis Tool), a new code for calculating chemical abundances in photoionized nebulae. The code carries out an analysis of lists of emission lines using long-established techniques to estimate the amount of interstellar extinction, calculate representative temperatures and densities, compute ionic abundances from both collisionally excited lines and recombination lines, and finally to estimate total elemental abundances using an ionization correction scheme. NEAT uses a Monte Carlo technique to robustly propagate uncer...
LDRD Final Report: Capabilities for Uncertainty in Predictive Science.
Energy Technology Data Exchange (ETDEWEB)
Phipps, Eric Todd; Eldred, Michael S; Salinger, Andrew G.; Webster, Clayton G.
2008-10-01
Predictive simulation of systems comprised of numerous interconnected, tightly coupled com-ponents promises to help solve many problems of scientific and national interest. Howeverpredictive simulation of such systems is extremely challenging due to the coupling of adiverse set of physical and biological length and time scales. This report investigates un-certainty quantification methods for such systems that attempt to exploit their structure togain computational efficiency. The traditional layering of uncertainty quantification aroundnonlinear solution processes is inverted to allow for heterogeneous uncertainty quantificationmethods to be applied to each component in a coupled system. Moreover this approachallows stochastic dimension reduction techniques to be applied at each coupling interface.The mathematical feasibility of these ideas is investigated in this report, and mathematicalformulations for the resulting stochastically coupled nonlinear systems are developed.3
Handling uncertainty and networked structure in robot control
Tamás, Levente
2015-01-01
This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams. Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at http://extras.springer.com. The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer...
Stereo-particle image velocimetry uncertainty quantification
Bhattacharya, Sayantan; Charonko, John J.; Vlachos, Pavlos P.
2017-01-01
Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current work, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. This stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric
An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method
Energy Technology Data Exchange (ETDEWEB)
Campolina, Daniel; Lima, Paulo Rubens I., E-mail: campolina@cdtn.br, E-mail: pauloinacio@cpejr.com.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil). Servico de Tecnologia de Reatores; Pereira, Claubia; Veloso, Maria Auxiliadora F., E-mail: claubia@nuclear.ufmg.br, E-mail: dora@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Dept. de Engenharia Nuclear
2015-07-01
Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k{sub eff} was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)
Maxallent: Maximizers of all Entropies and Uncertainty of Uncertainty
Gorban, A N
2013-01-01
The entropy maximum approach (Maxent) was developed as a minimization of the subjective uncertainty measured by the Boltzmann--Gibbs--Shannon entropy. Many new entropies have been invented in the second half of the 20th century. Now there exists a rich choice of entropies for fitting needs. This diversity of entropies gave rise to a Maxent "anarchism". Maxent approach is now the conditional maximization of an appropriate entropy for the evaluation of the probability distribution when our information is partial and incomplete. The rich choice of non-classical entropies causes a new problem: which entropy is better for a given class of applications? We understand entropy as a {\\em measure of uncertainty which increases in Markov processes.} In this work, we describe the most general ordering of the distribution space, with respect to which all continuous-time Markov processes are monotonic (the Markov order). For inference, this approach results in a {\\em set} of conditionally "most random" distributions. Each ...
An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring
Sankararaman, Shankar; Goebel, Kai
2014-01-01
This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.
Multifidelity, Multidisciplinary Design Under Uncertainty with Non-Intrusive Polynomial Chaos
West, Thomas K., IV; Gumbert, Clyde
2017-01-01
The primary objective of this work is to develop an approach for multifidelity uncertainty quantification and to lay the framework for future design under uncertainty efforts. In this study, multifidelity is used to describe both the fidelity of the modeling of the physical systems, as well as the difference in the uncertainty in each of the models. For computational efficiency, a multifidelity surrogate modeling approach based on non-intrusive polynomial chaos using the point-collocation technique is developed for the treatment of both multifidelity modeling and multifidelity uncertainty modeling. Two stochastic model problems are used to demonstrate the developed methodologies: a transonic airfoil model and multidisciplinary aircraft analysis model. The results of both showed the multifidelity modeling approach was able to predict the output uncertainty predicted by the high-fidelity model as a significant reduction in computational cost.
Applied research in uncertainty modeling and analysis
Ayyub, Bilal
2005-01-01
Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...
Visualizing Flow of Uncertainty through Analytical Processes.
Wu, Yingcai; Yuan, Guo-Xun; Ma, Kwan-Liu
2012-12-01
Uncertainty can arise in any stage of a visual analytics process, especially in data-intensive applications with a sequence of data transformations. Additionally, throughout the process of multidimensional, multivariate data analysis, uncertainty due to data transformation and integration may split, merge, increase, or decrease. This dynamic characteristic along with other features of uncertainty pose a great challenge to effective uncertainty-aware visualization. This paper presents a new framework for modeling uncertainty and characterizing the evolution of the uncertainty information through analytical processes. Based on the framework, we have designed a visual metaphor called uncertainty flow to visually and intuitively summarize how uncertainty information propagates over the whole analysis pipeline. Our system allows analysts to interact with and analyze the uncertainty information at different levels of detail. Three experiments were conducted to demonstrate the effectiveness and intuitiveness of our design.
Probabilistic forecasts based on radar rainfall uncertainty
Liguori, S.; Rico-Ramirez, M. A.
2012-04-01
The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at
Model Uncertainty for Bilinear Hysteretic Systems
DEFF Research Database (Denmark)
1984-01-01
In structural reliability analysis at least three types of uncertainty must be considered, namely physical uncertainty, statistical uncertainty, and model uncertainty (see e.g. Thoft·Christensen & Baker [1)). The physical uncertainty is usually modelled by a number of basic variables. The statist...... and reliability point of view. In section 4 it is shown how the probability of failure of a simple bilinear oscillator can be estimated and in section 5 it is demonstrated by numerical examples how model uncertainty can be included in the calculations.......In structural reliability analysis at least three types of uncertainty must be considered, namely physical uncertainty, statistical uncertainty, and model uncertainty (see e.g. Thoft·Christensen & Baker [1)). The physical uncertainty is usually modelled by a number of basic variables....... The statistical uncertainty -due to lack of information can e.g. be taken into account by describing the variables by predictive density functions, Veneziano [2). In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis...
Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting
Lee, Hyunsook; van Dyk, David A; Connors, Alanna; Drake, Jeremy J; Izem, Rima; Meng, Xiao-Li; Min, Shandong; Park, Taeyoung; Ratzlaff, Pete; Siemiginowska, Aneta; Zezas, Andreas
2011-01-01
While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a ...
Validation uncertainty of MATRA code for subchannel void distributions
Energy Technology Data Exchange (ETDEWEB)
Hwang, Dae-Hyun; Kim, S. J.; Kwon, H.; Seo, K. W. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2014-10-15
To extend code capability to the whole core subchannel analysis, pre-conditioned Krylov matrix solvers such as BiCGSTAB and GMRES are implemented in MATRA code as well as parallel computing algorithms using MPI and OPENMP. It is coded by fortran 90, and has some user friendly features such as graphic user interface. MATRA code was approved by Korean regulation body for design calculation of integral-type PWR named SMART. The major role subchannel code is to evaluate core thermal margin through the hot channel analysis and uncertainty evaluation for CHF predictions. In addition, it is potentially used for the best estimation of core thermal hydraulic field by incorporating into multiphysics and/or multi-scale code systems. In this study we examined a validation process for the subchannel code MATRA specifically in the prediction of subchannel void distributions. The primary objective of validation is to estimate a range within which the simulation modeling error lies. The experimental data for subchannel void distributions at steady state and transient conditions was provided on the framework of OECD/NEA UAM benchmark program. The validation uncertainty of MATRA code was evaluated for a specific experimental condition by comparing the simulation result and experimental data. A validation process should be preceded by code and solution verification. However, quantification of verification uncertainty was not addressed in this study. The validation uncertainty of the MATRA code for predicting subchannel void distribution was evaluated for a single data point of void fraction measurement at a 5x5 PWR test bundle on the framework of OECD UAM benchmark program. The validation standard uncertainties were evaluated as 4.2%, 3.9%, and 2.8% with the Monte-Carlo approach at the axial levels of 2216 mm, 2669 mm, and 3177 mm, respectively. The sensitivity coefficient approach revealed similar results of uncertainties but did not account for the nonlinear effects on the
Estimation of uncertainty for fatigue growth rate at cryogenic temperatures
Nyilas, Arman; Weiss, Klaus P.; Urbach, Elisabeth; Marcinek, Dawid J.
2014-01-01
Fatigue crack growth rate (FCGR) measurement data for high strength austenitic alloys at cryogenic environment suffer in general from a high degree of data scatter in particular at ΔK regime below 25 MPa√m. Using standard mathematical smoothing techniques forces ultimately a linear relationship at stage II regime (crack propagation rate versus ΔK) in a double log field called Paris law. However, the bandwidth of uncertainty relies somewhat arbitrary upon the researcher's interpretation. The present paper deals with the use of the uncertainty concept on FCGR data as given by GUM (Guidance of Uncertainty in Measurements), which since 1993 is a recommended procedure to avoid subjective estimation of error bands. Within this context, the lack of a true value addresses to evaluate the best estimate by a statistical method using the crack propagation law as a mathematical measurement model equation and identifying all input parameters. Each parameter necessary for the measurement technique was processed using the Gaussian distribution law by partial differentiation of the terms to estimate the sensitivity coefficients. The combined standard uncertainty determined for each term with its computed sensitivity coefficients finally resulted in measurement uncertainty of the FCGR test result. The described procedure of uncertainty has been applied within the framework of ITER on a recent FCGR measurement for high strength and high toughness Type 316LN material tested at 7 K using a standard ASTM proportional compact tension specimen. The determined values of Paris law constants such as C0 and the exponent m as best estimate along with the their uncertainty value may serve a realistic basis for the life expectancy of cyclic loaded members.
'spup' - an R package for uncertainty propagation in spatial environmental modelling
Sawicka, Kasia; Heuvelink, Gerard
2016-04-01
Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected static and interactive visualization methods that are understandable by non-experts with limited background in
Uncertainty Analysis in Humidity Measurements by the Psychrometer Method
Chen, Jiunyuan; Chen, Chiachung
2017-01-01
The most common and cheap indirect technique to measure relative humidity is by using psychrometer based on a dry and a wet temperature sensor. In this study, the measurement uncertainty of relative humidity was evaluated by this indirect method with some empirical equations for calculating relative humidity. Among the six equations tested, the Penman equation had the best predictive ability for the dry bulb temperature range of 15–50 °C. At a fixed dry bulb temperature, an increase in the wet bulb depression increased the error. A new equation for the psychrometer constant was established by regression analysis. This equation can be computed by using a calculator. The average predictive error of relative humidity was <0.1% by this new equation. The measurement uncertainty of the relative humidity affected by the accuracy of dry and wet bulb temperature and the numeric values of measurement uncertainty were evaluated for various conditions. The uncertainty of wet bulb temperature was the main factor on the RH measurement uncertainty. PMID:28216599
NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty
Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro
2014-01-01
Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than
Adjoint-Based Uncertainty Quantification with MCNP
Energy Technology Data Exchange (ETDEWEB)
Seifried, Jeffrey E. [Univ. of California, Berkeley, CA (United States)
2011-09-01
This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.
Roughness coefficient and its uncertainty in gravel-bed river
Institute of Scientific and Technical Information of China (English)
Ji-Sung KIM; Chan-Joo LEE; Won KIM; Yong-Jeon KIM
2010-01-01
Manning's roughness coefficient was estimated for a gravel-bed river reach using field measurements of water level and discharge,and the applicability of various methods used for estimation of the roughness coefficient was evaluated.Results show that the roughness coefficient tends to decrease with increasing discharge and water depth,and over a certain range it appears to remain constant.Comparison of roughness coefficients calculated by field measurement data with those estimated by other methods shows that,although the field-measured values provide approximate roughness coefficients for relatively large discharge,there seems to be rather high uncertainty due to the difference in resultant values.For this reason,uncertainty related to the roughness coefficient was analyzed in terms of change in computed variables.On average,a 20%increase of the roughness coefficient causes a 7% increase in the water depth and an 8% decrease in velocity,but there may be about a 15% increase in the water depth and an equivalent decrease in velocity for certain cross-sections in the study reach.Finally,the validity of estimated roughness coefficient based on field measurements was examined.A 10% error in discharge measurement may lead to more than 10% uncertainty in roughness coefficient estimation,but corresponding uncertainty in computed water depth and velocity is reduced to approximately 5%.Conversely,the necessity for roughness coefficient estimation by field measurement is confirmed.
Sibling Dependence, Uncertainty and Education
DEFF Research Database (Denmark)
Lilleør, Helene Bie
future agricultural employment. Given this dichtomy, the question is then: Does future income uncertainty influence the joint educational choice made by parents on behalf of their children and is it possible to test this on simple cross-sectional data? I extend a simple human capital portfolio model...... in the educational decision, which is consistent with a human capital portfolio theory of risk diversification and which cannot be explained by sibling rivalry over scarce resources for credit constrained households. The paper thus provides a complementary explanation to why enrolment rates in developing countries...... investigates the effects of future income uncertainty on sibling dependence in the schooling decisions of rural households in developing countries. Schooling tends to direct skills towards future urban employment, whereas traditional rural education or on-farm learning-by-doing tends to direct skills towards...
Uncertainty relation for mutual information
Schneeloch, James; Broadbent, Curtis J.; Howell, John C.
2014-12-01
We postulate the existence of a universal uncertainty relation between the quantum and classical mutual informations between pairs of quantum systems. Specifically, we propose that the sum of the classical mutual information, determined by two mutually unbiased pairs of observables, never exceeds the quantum mutual information. We call this the complementary-quantum correlation (CQC) relation and prove its validity for pure states, for states with one maximally mixed subsystem, and for all states when one measurement is minimally disturbing. We provide results of a Monte Carlo simulation suggesting that the CQC relation is generally valid. Importantly, we also show that the CQC relation represents an improvement to an entropic uncertainty principle in the presence of a quantum memory, and that it can be used to verify an achievable secret key rate in the quantum one-time pad cryptographic protocol.
Visualizing Summary Statistics and Uncertainty
Potter, K.
2010-08-12
The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.
Aspects of complementarity and uncertainty
Vathsan, Radhika; Qureshi, Tabish
2016-08-01
The two-slit experiment with quantum particles provides many insights into the behavior of quantum mechanics, including Bohr’s complementarity principle. Here, we analyze Einstein’s recoiling slit version of the experiment and show how the inevitable entanglement between the particle and the recoiling slit as a which-way detector is responsible for complementarity. We derive the Englert-Greenberger-Yasin duality from this entanglement, which can also be thought of as a consequence of sum-uncertainty relations between certain complementary observables of the recoiling slit. Thus, entanglement is an integral part of the which-way detection process, and so is uncertainty, though in a completely different way from that envisaged by Bohr and Einstein.
A Qualitative Approach to Uncertainty
Ghosh, Sujata; Velázquez-Quesada, Fernando R.
We focus on modelling dual epistemic attitudes (belief-disbelief, knowledge-ignorance, like-dislike) of an agent. This provides an interesting way to express different levels of uncertainties explicitly in the logical language. After introducing a dual modal framework, we discuss the different possibilities of an agent's attitude towards a proposition that can be expressed in this framework, and provide a preliminary look at the dynamics of the situation.
Information, uncertainty and holographic action
Dikken, Robbert-Jan
2016-01-01
In this short note we show through simple derivation the explicit relation between information flow and the theories of the emergence of space-time and gravity, specifically for Newton's second law of motion. Next, in a rather straightforward derivation the Heisenberg uncertainty relation is uncovered from the universal bound on information flow. A relation between the universal bound on information flow and the change in bulk action is also shown to exist.
Quantifying uncertainty from material inhomogeneity.
Energy Technology Data Exchange (ETDEWEB)
Battaile, Corbett Chandler; Emery, John M.; Brewer, Luke N.; Boyce, Brad Lee
2009-09-01
Most engineering materials are inherently inhomogeneous in their processing, internal structure, properties, and performance. Their properties are therefore statistical rather than deterministic. These inhomogeneities manifest across multiple length and time scales, leading to variabilities, i.e. statistical distributions, that are necessary to accurately describe each stage in the process-structure-properties hierarchy, and are ultimately the primary source of uncertainty in performance of the material and component. When localized events are responsible for component failure, or when component dimensions are on the order of microstructural features, this uncertainty is particularly important. For ultra-high reliability applications, the uncertainty is compounded by a lack of data describing the extremely rare events. Hands-on testing alone cannot supply sufficient data for this purpose. To date, there is no robust or coherent method to quantify this uncertainty so that it can be used in a predictive manner at the component length scale. The research presented in this report begins to address this lack of capability through a systematic study of the effects of microstructure on the strain concentration at a hole. To achieve the strain concentration, small circular holes (approximately 100 {micro}m in diameter) were machined into brass tensile specimens using a femto-second laser. The brass was annealed at 450 C, 600 C, and 800 C to produce three hole-to-grain size ratios of approximately 7, 1, and 1/7. Electron backscatter diffraction experiments were used to guide the construction of digital microstructures for finite element simulations of uniaxial tension. Digital image correlation experiments were used to qualitatively validate the numerical simulations. The simulations were performed iteratively to generate statistics describing the distribution of plastic strain at the hole in varying microstructural environments. In both the experiments and simulations, the
Strong majorization entropic uncertainty relations
Energy Technology Data Exchange (ETDEWEB)
Rudnicki, Lukasz [Freiburg Institute for Advanced Studies, Albert-Ludwigs University of Freiburg, Albertstrasse 19, 79104 Freiburg (Germany); Center for Theoretical Physics, Polish Academy of Sciences, Aleja Lotnikow 32/46, PL-02-668 Warsaw (Poland); Puchala, Zbigniew [Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice (Poland); Institute of Physics, Jagiellonian University, ul Reymonta 4, 30-059 Krakow (Poland); Zyczkowski, Karol [Center for Theoretical Physics, Polish Academy of Sciences, Aleja Lotnikow 32/46, PL-02-668 Warsaw (Poland); Institute of Physics, Jagiellonian University, ul Reymonta 4, 30-059 Krakow (Poland)
2014-07-01
We present new entropic uncertainty relations in a finite-dimensional Hilbert space. Using the majorization technique we derive several explicit lower bounds for the sum of two Renyi entropies of the same order. Obtained bounds are expressed in terms of the largest singular values of given unitary matrices. Numerical simulations with random unitary matrices show that our bound is almost always stronger than the well known result of Maassen and Uffink.
CRISIS FOCUS Uncertainty and Flexibility
Institute of Scientific and Technical Information of China (English)
2008-01-01
As the world continued to watch the unfolding financial and economic crises this month, Robert Zoellick, President of the World Bank, arrived in China for discussions on how the country can help support the global economy and the efforts it has taken to strengthen its own recovery. Zoellick, who had seen many uncertainties in 2009, called for China to be flexible with its macroeconomic policy. He made the following comments at a press conference in Beijing on December 15. Edited excerpts follow:
Accommodating Uncertainty in Prior Distributions
Energy Technology Data Exchange (ETDEWEB)
Picard, Richard Roy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Vander Wiel, Scott Alan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-01-19
A fundamental premise of Bayesian methodology is that a priori information is accurately summarized by a single, precisely de ned prior distribution. In many cases, especially involving informative priors, this premise is false, and the (mis)application of Bayes methods produces posterior quantities whose apparent precisions are highly misleading. We examine the implications of uncertainty in prior distributions, and present graphical methods for dealing with them.
Institute of Scientific and Technical Information of China (English)
2010-01-01
@@ The global economy is expected to grow by 2-3 percent in 2010,according to the Yellow Book of International Economy for 2010 released on December 24 by the Chinese Academy of Social Sciences(CASS).Zhang Yuyan,Editor in Chief of the yellow book and Director of the Institute of World Economics and Politics at CASS,offered his insights into uncertainties in the world economy in 2010 at the launching ceremony for the yellow book.Edited excerpts follow:
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The global economy is expected to grow by 2-3 percent in 2010,according to the Yellow Book of International Economy for 2010 released on December 24 by the Chinese Academy of Social Sciences (CASS).Zhang Yuyan,Editor in Chief of the yellow book and Director of the Institute of World Economics and Politics at CASS,offered his insights into uncertainties in the world economy in 2010 at the launching ceremony for the yellow book.
Uncertainty Relation from Holography Principle
Chen, Jia-Zhong; Jia, Duoje
2004-01-01
We propose that the information and entropy of an isolated system are two sides of one coin in the sense that they can convert into each other by measurement and evolution of the system while the sum of them is identically conserved. The holographic principle is reformulated in the way that this conserved sum is bounded by a quarter of the area A of system boundary. Uncertainty relation is derived from the holographic principle.
Computational Modeling of Uncertainty Avoidance in Consumer Behavior
Roozmand, O.; Ghasem-Aghaee, N.; Nematbakhsh, M.A.; Baraani, A.; Hofstede, G.J.
2011-01-01
Human purchasing behavior is affected by many influential factors. Culture at macro-level and personality at micro-level influence consumer purchasing behavior. People of different cultures tend to accept the values of their own group and consequently have different purchasing behavior. Also, people
Multilevel model reduction for uncertainty quantification in computational structural dynamics
Ezvan, O.; Batou, A.; Soize, C.; Gagliardini, L.
2016-11-01
Within the continuum mechanics framework, there are two main approaches to model interfaces: classical cohesive zone modeling (CZM) and interface elasticity theory. The classical CZM deals with geometrically non-coherent interfaces for which the constitutive relation is expressed in terms of traction-separation laws. However, CZM lacks any response related to the stretch of the mid-plane of the interface. This issue becomes problematic particularly at small scales with increasing interface area to bulk volume ratios, where interface elasticity is no longer negligible. The interface elasticity theory, in contrast to CZM, deals with coherent interfaces that are endowed with their own energetic structures, and thus is capable of capturing elastic resistance to tangential stretch. Nonetheless, the interface elasticity theory suffers from the lack of inelastic material response, regardless of the strain level. The objective of this contribution therefore is to introduce a generalized mechanical interface model that couples both the elastic response along the interface and the cohesive response across the interface whereby interface degradation is taken into account. The material degradation of the interface mid-plane is captured by a non-local damage model of integral-type. The out-of-plane decohesion is described by a classical cohesive zone model. These models are then coupled through their corresponding damage variables. The non-linear governing equations and the weak forms thereof are derived. The numerical implementation is carried out using the finite element method and consistent tangents are derived. Finally, a series of numerical examples is studied to provide further insight into the problem and to carefully elucidate key features of the proposed theory.
Blade tip timing (BTT) uncertainties
Russhard, Pete
2016-06-01
Blade Tip Timing (BTT) is an alternative technique for characterising blade vibration in which non-contact timing probes (e.g. capacitance or optical probes), typically mounted on the engine casing (figure 1), and are used to measure the time at which a blade passes each probe. This time is compared with the time at which the blade would have passed the probe if it had been undergoing no vibration. For a number of years the aerospace industry has been sponsoring research into Blade Tip Timing technologies that have been developed as tools to obtain rotor blade tip deflections. These have been successful in demonstrating the potential of the technology, but rarely produced quantitative data, along with a demonstration of a traceable value for measurement uncertainty. BTT technologies have been developed under a cloak of secrecy by the gas turbine OEM's due to the competitive advantages it offered if it could be shown to work. BTT measurements are sensitive to many variables and there is a need to quantify the measurement uncertainty of the complete technology and to define a set of guidelines as to how BTT should be applied to different vehicles. The data shown in figure 2 was developed from US government sponsored program that bought together four different tip timing system and a gas turbine engine test. Comparisons showed that they were just capable of obtaining measurement within a +/-25% uncertainty band when compared to strain gauges even when using the same input data sets.
Managing project risks and uncertainties
Directory of Open Access Journals (Sweden)
Mike Mentis
2015-01-01
Full Text Available This article considers threats to a project slipping on budget, schedule and fit-for-purpose. Threat is used here as the collective for risks (quantifiable bad things that can happen and uncertainties (poorly or not quantifiable bad possible events. Based on experience with projects in developing countries this review considers that (a project slippage is due to uncertainties rather than risks, (b while eventuation of some bad things is beyond control, managed execution and oversight are still the primary means to keeping within budget, on time and fit-for-purpose, (c improving project delivery is less about bigger and more complex and more about coordinated focus, effectiveness and developing thought-out heuristics, and (d projects take longer and cost more partly because threat identification is inaccurate, the scope of identified threats is too narrow, and the threat assessment product is not integrated into overall project decision-making and execution. Almost by definition, what is poorly known is likely to cause problems. Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage, but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns. Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals. This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.
Characterizing Epistemic Uncertainty for Launch Vehicle Designs
Novack, Steven D.; Rogers, Jim; Al Hassan, Mohammad; Hark, Frank
2016-01-01
NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk, and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results, and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods, such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty, are rendered obsolete, since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods. This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper describes how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.
Modeling Heterogeneity in Networks using Uncertainty Quantification Tools
Rajendran, Karthikeyan; Siettos, Constantinos I; Laing, Carlo R; Kevrekidis, Ioannis G
2015-01-01
Using the dynamics of information propagation on a network as our illustrative example, we present and discuss a systematic approach to quantifying heterogeneity and its propagation that borrows established tools from Uncertainty Quantification. The crucial assumption underlying this mathematical and computational "technology transfer" is that the evolving states of the nodes in a network quickly become correlated with the corresponding node "identities": features of the nodes imparted by the network structure (e.g. the node degree, the node clustering coefficient). The node dynamics thus depend on heterogeneous (rather than uncertain) parameters, whose distribution over the network results from the network structure. Knowing these distributions allows us to obtain an efficient coarse-grained representation of the network state in terms of the expansion coefficients in suitable orthogonal polynomials. This representation is closely related to mathematical/computational tools for uncertainty quantification (th...
Probabilistic Load Flow Considering Wind Generation Uncertainty
Directory of Open Access Journals (Sweden)
R. Ramezani
2011-10-01
Full Text Available Renewable energy sources, such as wind, solar and hydro, are increasingly incorporated into power grids, as a direct consequence of energy and environmental issues. These types of energies are variable and intermittent by nature and their exploitation introduces uncertainties into the power grid. Therefore, probabilistic analysis of the system performance is of significant interest. This paper describes a new approach to Probabilistic Load Flow (PLF by modifying the Two Point Estimation Method (2PEM to cover some drawbacks of other currently used methods. The proposed method is examined using two case studies, the IEEE 9-bus and the IEEE 57-bus test systems. In order to justify the effectiveness of the method, numerical comparison with Monte Carlo Simulation (MCS method is presented. Simulation results indicate that the proposed method significantly reduces the computational burden while maintaining a high level of accuracy. Moreover, that the unsymmetrical 2PEM has a higher level of accuracy than the symmetrical 2PEM with equal computing burden, when the Probability Density Function (PDF of uncertain variables is asymmetric.
Improved Approximations for Multiprocessor Scheduling Under Uncertainty
Crutchfield, Christopher; Fineman, Jeremy T; Karger, David R; Scott, Jacob
2008-01-01
This paper presents improved approximation algorithms for the problem of multiprocessor scheduling under uncertainty, or SUU, in which the execution of each job may fail probabilistically. This problem is motivated by the increasing use of distributed computing to handle large, computationally intensive tasks. In the SUU problem we are given n unit-length jobs and m machines, a directed acyclic graph G of precedence constraints among jobs, and unrelated failure probabilities q_{ij} for each job j when executed on machine i for a single timestep. Our goal is to find a schedule that minimizes the expected makespan, which is the expected time at which all jobs complete. Lin and Rajaraman gave the first approximations for this NP-hard problem for the special cases of independent jobs, precedence constraints forming disjoint chains, and precedence constraints forming trees. In this paper, we present asymptotically better approximation algorithms. In particular, we give an O(loglog min(m,n))-approximation for indep...
Facility Measurement Uncertainty Analysis at NASA GRC
Stephens, Julia; Hubbard, Erin
2016-01-01
This presentation provides and overview of the measurement uncertainty analysis currently being implemented in various facilities at NASA GRC. This presentation includes examples pertinent to the turbine engine community (mass flow and fan efficiency calculation uncertainties.
Energy Technology Data Exchange (ETDEWEB)
Isukapalli, S.S.; Georgopoulos, P.G. [Environmental and Occupational Health Sciences Inst., Piscataway, NJ (United States)
1997-12-31
Uncertainty in biogenic emission estimates and photochemical reaction rates can contribute significantly to modeling error in Photochemical Air Quality Simulation Models (PAQSMs). Uncertainties in isoprene emissions from biogenic sources, and isoprene atmospheric degradation rates have recently received considerable attention with respect to control strategy selection for the reduction of tropospheric ozone levels. This study addresses the effects of uncertainties in isoprene emissions and reaction rates on ambient ozone concentrations predicted by PAQSMs. Since PAQSMs are computationally intensive, propagation of uncertainty in reaction rate constants using traditional methods, such as Monte Carlo methods, is not computationally feasible. Here, a novel computationally efficient method of uncertainty analysis, called the Stochastic Response Surface Method (SRSM), is applied to propagate uncertainty in isoprene emissions and reaction rate parameters. Case studies include estimation of uncertainty in ozone concentrations predicted by (a) a box-model, (b) a plume trajectory model, the Reactive Plume Model (RPM), and (c) an urban-to-regional scale grid model, the Urban Airshed Model (UAM). The results of this analysis are used to characterize the relative importance of uncertainties in isoprene emissions and reaction rates on ozone levels for a wide range of conditions. Furthermore, this work demonstrates the applicability of the SRSM uncertainty propagation methodology to computationally intensive models such as the UAM.
Quantifying Snow Volume Uncertainty from Repeat Terrestrial Laser Scanning Observations
Gadomski, P. J.; Hartzell, P. J.; Finnegan, D. C.; Glennie, C. L.; Deems, J. S.
2014-12-01
Terrestrial laser scanning (TLS) systems are capable of providing rapid, high density, 3D topographic measurements of snow surfaces from increasing standoff distances. By differencing snow surface with snow free measurements within a common scene, snow depths and volumes can be estimated. These data can support operational water management decision-making when combined with measured or modeled snow densities to estimate basin water content, evaluate in-situ data, or drive operational hydrologic models. In addition, change maps from differential TLS scans can also be used to support avalanche control operations to quantify loading patterns for both pre-control planning and post-control assessment. However, while methods for computing volume from TLS point cloud data are well documented, a rigorous quantification of the volumetric uncertainty has yet to be presented. Using repeat TLS data collected at the Arapahoe Basin Ski Area in Summit County, Colorado, we demonstrate the propagation of TLS point measurement and cloud registration uncertainties into 3D covariance matrices at the point level. The point covariances are then propagated through a volume computation to arrive at a single volume uncertainty value. Results from two volume computation methods are compared and the influence of data voids produced by occlusions examined.
Errors and Uncertainty in Physics Measurement.
Blasiak, Wladyslaw
1983-01-01
Classifies errors as either systematic or blunder and uncertainties as either systematic or random. Discusses use of error/uncertainty analysis in direct/indirect measurement, describing the process of planning experiments to ensure lowest possible uncertainty. Also considers appropriate level of error analysis for high school physics students'…
Uncertainty Sets For Wind Power Generation
Dvorkin, Yury; Lubin, Miles; Backhaus, Scott; Chertkov, Michael
2015-01-01
As penetration of wind power generation increases, system operators must account for its stochastic nature in a reliable and cost-efficient manner. These conflicting objectives can be traded-off by accounting for the variability and uncertainty of wind power generation. This letter presents a new methodology to estimate uncertainty sets for parameters of probability distributions that capture wind generation uncertainty and variability.
Identifying the Rhetoric of Uncertainty Reduction.
Williams, David E.
Offering a rhetorical perspective of uncertainty reduction, this paper (1) discusses uncertainty reduction theory and dramatism; (2) identifies rhetorical strategies inherent in C. W. Berger and R. J. Calabrese's theory; (3) extends predicted outcome value to influenced outcome value; and (4) argues that the goal of uncertainty reduction and…
Simplified transmitter design for MIMO systems with channel uncertainty
Institute of Scientific and Technical Information of China (English)
DU Juan; KANG Gui-xia; ZHANG Ping
2009-01-01
This article investigates transmitter design in Rayleigh fading multiple input multiple output (MIMO) channels with spatial correlation when there are channel uncertainties caused by a combined effect of channel estimation error and limited feedback. To overcome the high computational complexity of the optimal transmit power allocation, a simple and suboptimal allocation is proposed by exploiting the transmission constraint and differentiating a bound based on Jensen inequality on the channel capacity. The simulation results show that the mutual information corresponding to the proposed power allocation closely approaches the channel capacity corresponding to the optimal one and meanwhile the computational complexity is greatly reduced.
Confronting uncertainty in flood damage predictions
Schröter, Kai; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno
2015-04-01
Reliable flood damage models are a prerequisite for the practical usefulness of the model results. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005 and 2006, in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.
Uncertainty and validation. Effect of user interpretation on uncertainty estimates
Energy Technology Data Exchange (ETDEWEB)
Kirchner, G. [Univ. of Bremen (Germany); Peterson, R. [AECL, Chalk River, ON (Canada)] [and others
1996-11-01
Uncertainty in predictions of environmental transfer models arises from, among other sources, the adequacy of the conceptual model, the approximations made in coding the conceptual model, the quality of the input data, the uncertainty in parameter values, and the assumptions made by the user. In recent years efforts to quantify the confidence that can be placed in predictions have been increasing, but have concentrated on a statistical propagation of the influence of parameter uncertainties on the calculational results. The primary objective of this Working Group of BIOMOVS II was to test user's influence on model predictions on a more systematic basis than has been done before. The main goals were as follows: To compare differences between predictions from different people all using the same model and the same scenario description with the statistical uncertainties calculated by the model. To investigate the main reasons for different interpretations by users. To create a better awareness of the potential influence of the user on the modeling results. Terrestrial food chain models driven by deposition of radionuclides from the atmosphere were used. Three codes were obtained and run with three scenarios by a maximum of 10 users. A number of conclusions can be drawn some of which are general and independent of the type of models and processes studied, while others are restricted to the few processes that were addressed directly: For any set of predictions, the variation in best estimates was greater than one order of magnitude. Often the range increased from deposition to pasture to milk probably due to additional transfer processes. The 95% confidence intervals about the predictions calculated from the parameter distributions prepared by the participants did not always overlap the observations; similarly, sometimes the confidence intervals on the predictions did not overlap. Often the 95% confidence intervals of individual predictions were smaller than the
Measurement uncertainty evaluation of conicity error inspected on CMM
Wang, Dongxia; Song, Aiguo; Wen, Xiulan; Xu, Youxiong; Qiao, Guifang
2016-01-01
The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification(GPS), the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm(IIEA) is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement(GUM) method to evaluate measurement uncertainty. Adaptive Monte Carlo method(AMCM) is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine(CMM). The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error(MZCE) rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly.
Quantum Uncertainty and Fundamental Interactions
Directory of Open Access Journals (Sweden)
Tosto S.
2013-04-01
Full Text Available The paper proposes a simplified theoretical approach to infer some essential concepts on the fundamental interactions between charged particles and their relative strengths at comparable energies by exploiting the quantum uncertainty only. The worth of the present approach relies on the way of obtaining the results, rather than on the results themselves: concepts today acknowledged as fingerprints of the electroweak and strong interactions appear indeed rooted in the same theoretical frame including also the basic principles of special and general relativity along with the gravity force.
Modelling of Transport Projects Uncertainties
DEFF Research Database (Denmark)
Salling, Kim Bang; Leleur, Steen
2009-01-01
This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... investment costs, with a quantitative risk analysis based on Monte Carlo simulation and to make use of a set of exploratory scenarios. The analysis is carried out by using the CBA-DK model representing the Danish standard approach to socio-economic cost-benefit analysis. Specifically, the paper proposes...
Modelling of Transport Projects Uncertainties
DEFF Research Database (Denmark)
Salling, Kim Bang; Leleur, Steen
2012-01-01
This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... investment costs, with a quantitative risk analysis based on Monte Carlo simulation and to make use of a set of exploratory scenarios. The analysis is carried out by using the CBA-DK model representing the Danish standard approach to socio-economic cost-benefit analysis. Specifically, the paper proposes...
Connectivity graphs of uncertainty regions
Chambers, Erin; Lenchner, Jonathan; Sember, Jeff; Srinivasan, Venkatesh; Stege, Ulrike; Stolpner, Svetlana; Weibel, Christophe; Whitesides, Sue
2010-01-01
We study a generalization of the well known bottleneck spanning tree problem called "Best Case Connectivity with Uncertainty": Given a family of geometric regions, choose one point per region, such that the length of the longest edge in a spanning tree of a disc intersection graph is minimized. We show that this problem is NP-hard even for very simple scenarios such as line segments and squares. We also give exact and approximation algorithms for the case of line segments and unit discs respectively.
Uncertainty assessment using uncalibrated objects:
DEFF Research Database (Denmark)
Meneghello, R.; Savio, Enrico; Larsen, Erik;
This report is made as a part of the project Easytrac, an EU project under the programme: Competitive and Sustainable Growth: Contract No: G6RD-CT-2000-00188, coordinated by UNIMETRIK S.A. (Spain). The project is concerned with low uncertainty calibrations on coordinate measuring machines....... The Centre for Geometrical Metrology (CGM) at the Technical University of Denmark takes care of free form measurements, in collaboration with DIMEG, University of Padova, Italy and Unilab Laboratori Industriali Srl, Italy. The present report describes the calibration of a bevel gear using the method...
Uncertainty Relation and Inseparability Criterion
Goswami, Ashutosh K.; Panigrahi, Prasanta K.
2016-11-01
We investigate the Peres-Horodecki positive partial transpose criterion in the context of conserved quantities and derive a condition of inseparability for a composite bipartite system depending only on the dimensions of its subsystems, which leads to a bi-linear entanglement witness for the two qubit system. A separability inequality using generalized Schrodinger-Robertson uncertainty relation taking suitable operators, has been derived, which proves to be stronger than the bi-linear entanglement witness operator. In the case of mixed density matrices, it identically distinguishes the separable and non separable Werner states.
Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam
Directory of Open Access Journals (Sweden)
Dao Nguyen Khoi
2015-07-01
Full Text Available Hydrological models play vital roles in management of water resources. However, the calibration of the hydrological models is a large challenge because of the uncertainty involved in the large number of parameters. In this study, four uncertainty analysis methods, including Generalized Likelihood Uncertainty Estimation (GLUE, Parameter Solution (ParaSol, Particle Swarm Optimization (PSO, and Sequential Uncertainty Fitting (SUFI-2, were employed to perform parameter uncertainty analysis of streamflow simulation in the Srepok River Catchment by using the Soil and Water Assessment Tool (SWAT model. The four methods were compared in terms of the model prediction uncertainty, the model performance, and the computational efficiency. The results showed that the SUFI-2 method has the advantages in the model calibration and uncertainty analysis. This technique could be run with the smallest of simulation runs to achieve good prediction uncertainty bands and model performance. This technique could be run with the smallest of simulation runs to achieve good prediction uncertainty bands and model performance.
Uncertainty in Interval Type-2 Fuzzy Systems
Directory of Open Access Journals (Sweden)
Sadegh Aminifar
2013-01-01
Full Text Available This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions differentiate from T1MFs (type-1 membership functions by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs. Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs. Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done.
Angevine, W. M.; Brioude, J. F.; McKeen, S. A.
2014-12-01
Lagrangian particle dispersion models, used to estimate emissions from observations, require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30-40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15-20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis. It is not yet known exactly how these uncertainties will propagate through inversions to affect emissions estimates.
Aspects of universally valid Heisenberg uncertainty relation
Fujikawa, Kazuo
2012-01-01
A numerical illustration of a universally valid Heisenberg uncertainty relation, which was proposed recently, is presented by using the experimental data on spin-measurements by J. Erhart, et al.[ Nature Phys. {\\bf 8}, 185 (2012)]. This uncertainty relation is closely related to a modified form of the Arthurs-Kelly uncertainty relation which is also tested by the spin-measurements. The universally valid Heisenberg uncertainty relation always holds, but both the modified Arthurs-Kelly uncertainty relation and Heisenberg's error-disturbance relation proposed by Ozawa, which was analyzed in the original experiment, fail in the present context of spin-measurements, and the cause of their failure is identified with the assumptions of unbiased measurement and disturbance. It is also shown that all the universally valid uncertainty relations are derived from Robertson's relation and thus the essence of the uncertainty relation is exhausted by Robertson's relation as is widely accepted.
Uncertainty in geological and hydrogeological data
Directory of Open Access Journals (Sweden)
B. Nilsson
2007-09-01
Full Text Available Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification.
Uncertainty Relations and Possible Experience
Directory of Open Access Journals (Sweden)
Gregg Jaeger
2016-06-01
Full Text Available The uncertainty principle can be understood as a condition of joint indeterminacy of classes of properties in quantum theory. The mathematical expressions most closely associated with this principle have been the uncertainty relations, various inequalities exemplified by the well known expression regarding position and momentum introduced by Heisenberg. Here, recent work involving a new sort of “logical” indeterminacy principle and associated relations introduced by Pitowsky, expressable directly in terms of probabilities of outcomes of measurements of sharp quantum observables, is reviewed and its quantum nature is discussed. These novel relations are derivable from Boolean “conditions of possible experience” of the quantum realm and have been considered both as fundamentally logical and as fundamentally geometrical. This work focuses on the relationship of indeterminacy to the propositions regarding the values of discrete, sharp observables of quantum systems. Here, reasons for favoring each of these two positions are considered. Finally, with an eye toward future research related to indeterminacy relations, further novel approaches grounded in category theory and intended to capture and reconceptualize the complementarity characteristics of quantum propositions are discussed in relation to the former.
Path planning under spatial uncertainty.
Wiener, Jan M; Lafon, Matthieu; Berthoz, Alain
2008-04-01
In this article, we present experiments studying path planning under spatial uncertainties. In the main experiment, the participants' task was to navigate the shortest possible path to find an object hidden in one of four places and to bring it to the final destination. The probability of finding the object (probability matrix) was different for each of the four places and varied between conditions. Givensuch uncertainties about the object's location, planning a single path is not sufficient. Participants had to generate multiple consecutive plans (metaplans)--for example: If the object is found in A, proceed to the destination; if the object is not found, proceed to B; and so on. The optimal solution depends on the specific probability matrix. In each condition, participants learned a different probability matrix and were then asked to report the optimal metaplan. Results demonstrate effective integration of the probabilistic information about the object's location during planning. We present a hierarchical planning scheme that could account for participants' behavior, as well as for systematic errors and differences between conditions.
Uncertainty quantification in virtual surgery predictions for single ventricle palliation
Schiavazzi, Daniele; Marsden, Alison
2014-11-01
Hemodynamic results from numerical simulations of physiology in patients are invariably presented as deterministic quantities without assessment of associated confidence. Recent advances in cardiovascular simulation and Uncertainty Analysis can be leveraged to challenge this paradigm and to quantify the variability of output quantities of interest, of paramount importance to complement clinical decision making. Physiological variability and errors are responsible for the uncertainty typically associated with measurements in the clinic; starting from a characterization of these quantities in probability, we present applications in the context of estimating the distributions of lumped parameters in 0D models of single-ventricle circulation. We also present results in virtual Fontan palliation surgery, where the variability of both local and systemic hemodynamic indicators is inferred from the uncertainty in pre-operative clinical measurements. Efficient numerical algorithms are required to mitigate the computational cost of propagating the uncertainty through multiscale coupled 0D-3D models of pulsatile flow at the cavopulmonary connection. This work constitutes a first step towards systematic application of robust numerical simulations to virtual surgery predictions.
Uncertainty quantification for large-scale ocean circulation predictions.
Energy Technology Data Exchange (ETDEWEB)
Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik
2010-09-01
Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.
Relationships for Cost and Uncertainty of Decision Trees
Chikalov, Igor
2013-01-01
This chapter is devoted to the design of new tools for the study of decision trees. These tools are based on dynamic programming approach and need the consideration of subtables of the initial decision table. So this approach is applicable only to relatively small decision tables. The considered tools allow us to compute: 1. Theminimum cost of an approximate decision tree for a given uncertainty value and a cost function. 2. The minimum number of nodes in an exact decision tree whose depth is at most a given value. For the first tool we considered various cost functions such as: depth and average depth of a decision tree and number of nodes (and number of terminal and nonterminal nodes) of a decision tree. The uncertainty of a decision table is equal to the number of unordered pairs of rows with different decisions. The uncertainty of approximate decision tree is equal to the maximum uncertainty of a subtable corresponding to a terminal node of the tree. In addition to the algorithms for such tools we also present experimental results applied to various datasets acquired from UCI ML Repository [4]. © Springer-Verlag Berlin Heidelberg 2013.
Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations
Diez, Carlos Javier; Hoefer, Axel; Porsch, Dieter; Cabellos, Oscar
2014-01-01
Several methodologies using different levels of approximations have been developed for propagating nuclear data uncertainties in nuclear burn-up simulations. Most methods fall into the two broad classes of Monte Carlo approaches, which are exact apart from statistical uncertainties but require additional computation time, and first order perturbation theory approaches, which are efficient for not too large numbers of considered response functions but only applicable for sufficiently small nuclear data uncertainties. Some methods neglect isotopic composition uncertainties induced by the depletion steps of the simulations, others neglect neutron flux uncertainties, and the accuracy of a given approximation is often very hard to quantify. In order to get a better sense of the impact of different approximations, this work aims to compare results obtained based on different approximate methodologies with an exact method, namely the NUDUNA Monte Carlo based approach developed by AREVA GmbH. In addition, the impact ...
A Multi-Model Approach for Uncertainty Propagation and Model Calibration in CFD Applications
Wang, Jian-xun; Xiao, Heng
2015-01-01
Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particular obstacle for uncertainty quantifications in CFD problems is the large model discrepancies associated with the CFD models used for uncertainty propagation. Neglecting or improperly representing the model discrepancies leads to inaccurate and distorted uncertainty distribution for the Quantities of Interest. High-fidelity models, being accurate yet expensive, can accommodate only a small ensemble of simulations and thus lead to large interpolation errors and/or sampling errors; low-fidelity models can propagate a large ensemble, but can introduce large modeling errors. In this work, we propose a multi-model strategy to account for the influences of model discrepancies in uncertainty propagation and to reduce their impact on the predictions. Specifically, we take advantage of CFD models of multiple fidelities to estimate the model ...
Evaluating the uncertainty of input quantities in measurement models
Possolo, Antonio; Elster, Clemens
2014-06-01
The Guide to the Expression of Uncertainty in Measurement (GUM) gives guidance about how values and uncertainties should be assigned to the input quantities that appear in measurement models. This contribution offers a concrete proposal for how that guidance may be updated in light of the advances in the evaluation and expression of measurement uncertainty that were made in the course of the twenty years that have elapsed since the publication of the GUM, and also considering situations that the GUM does not yet contemplate. Our motivation is the ongoing conversation about a new edition of the GUM. While generally we favour a Bayesian approach to uncertainty evaluation, we also recognize the value that other approaches may bring to the problems considered here, and focus on methods for uncertainty evaluation and propagation that are widely applicable, including to cases that the GUM has not yet addressed. In addition to Bayesian methods, we discuss maximum-likelihood estimation, robust statistical methods, and measurement models where values of nominal properties play the same role that input quantities play in traditional models. We illustrate these general-purpose techniques in concrete examples, employing data sets that are realistic but that also are of conveniently small sizes. The supplementary material available online lists the R computer code that we have used to produce these examples (stacks.iop.org/Met/51/3/339/mmedia). Although we strive to stay close to clause 4 of the GUM, which addresses the evaluation of uncertainty for input quantities, we depart from it as we review the classes of measurement models that we believe are generally useful in contemporary measurement science. We also considerably expand and update the treatment that the GUM gives to Type B evaluations of uncertainty: reviewing the state-of-the-art, disciplined approach to the elicitation of expert knowledge, and its encapsulation in probability distributions that are usable in
Lithological Uncertainty Expressed by Normalized Compression Distance
Jatnieks, J.; Saks, T.; Delina, A.; Popovs, K.
2012-04-01
prediction by partial matching (PPM), used for computing the NCD metric, is highly dependant on context. We assign unique symbols for aggregate lithology types and serialize the borehole logs into text strings, where the string length represents a normalized borehole depth. This encoding ensures that both lithology types as well as depth and sequence of strata is comparable in a form most native to the universal data compression software that calculates the pairwise NCD dissimilarity matrix. The NCD results can be used for generalization of the Quaternary structure using spatial clustering followed by a Voronoi tessellation using boreholes as generator points. After dissolving cluster membership identifiers of the borehole Voronoi polygons in GIS environment, regions representing similar lithological structure can be visualized. The exact number of regions and their homogeneity depends on parameters of the clustering solution. This study is supported by the European Social Fund project No. 2009/0212/1DP/1.1.1.2.0/09/APIA/VIAA/060 Keywords: geological uncertainty, lithological uncertainty, generalization, information distance, normalized compression distance, data compression
Linear minimax estimation for random vectors with parametric uncertainty
Bitar, E
2010-06-01
In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t).
Arnold, Dan; Demyanov, Vasily; Christie, Mike; Bakay, Alexander; Gopa, Konstantin
2016-10-01
Assessing the change in uncertainty in reservoir production forecasts over field lifetime is rarely undertaken because of the complexity of joining together the individual workflows. This becomes particularly important in complex fields such as naturally fractured reservoirs. The impact of this problem has been identified in previous and many solutions have been proposed but never implemented on complex reservoir problems due to the computational cost of quantifying uncertainty and optimising the reservoir development, specifically knowing how many and what kind of simulations to run. This paper demonstrates a workflow that propagates uncertainty throughout field lifetime, and into the decision making process by a combination of a metric-based approach, multi-objective optimisation and Bayesian estimation of uncertainty. The workflow propagates uncertainty estimates from appraisal into initial development optimisation, then updates uncertainty through history matching and finally propagates it into late-life optimisation. The combination of techniques applied, namely the metric approach and multi-objective optimisation, help evaluate development options under uncertainty. This was achieved with a significantly reduced number of flow simulations, such that the combined workflow is computationally feasible to run for a real-field problem. This workflow is applied to two synthetic naturally fractured reservoir (NFR) case studies in appraisal, field development, history matching and mid-life EOR stages. The first is a simple sector model, while the second is a more complex full field example based on a real life analogue. This study infers geological uncertainty from an ensemble of models that are based on the carbonate Brazilian outcrop which are propagated through the field lifetime, before and after the start of production, with the inclusion of production data significantly collapsing the spread of P10-P90 in reservoir forecasts. The workflow links uncertainty
Assessing and propagating uncertainty in model inputs in corsim
Energy Technology Data Exchange (ETDEWEB)
Molina, G.; Bayarri, M. J.; Berger, J. O.
2001-07-01
CORSIM is a large simulator for vehicular traffic, and is being studied with respect to its ability to successfully model and predict behavior of traffic in a 36 block section of Chicago. Inputs to the simulator include information about street configuration, driver behavior, traffic light timing, turning probabilities at each corner and distributions of traffic ingress into the system. This work is described in more detail in the article Fast Simulators for Assessment and Propagation of Model Uncertainty also in these proceedings. The focus of this conference poster is on the computational aspects of this problem. In particular, we address the description of the full conditional distributions needed for implementation of the MCMC algorithm and, in particular, how the constraints can be incorporated; details concerning the run time and convergence of the MCMC algorithm; and utilisation of the MCMC output for prediction and uncertainty analysis concerning the CORSIM computer model. As this last is the ultimate goal, it is worth emphasizing that the incorporation of all uncertainty concerning inputs can significantly affect the model predictions. (Author)
Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.
Directory of Open Access Journals (Sweden)
Claudia Schillings
2015-08-01
Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
Stochastic reduced order models for inverse problems under uncertainty.
Warner, James E; Aquino, Wilkins; Grigoriu, Mircea D
2015-03-01
This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well.
Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation
Schiavazzi, Daniele; Marsden, Alison
2015-11-01
Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.
Uncertainty, incompleteness, chance, and design
Sols, Fernando
2013-01-01
The 20th century has revealed two important limitations of scientific knowledge. On the one hand, the combination of Poincar\\'e's nonlinear dynamics and Heisenberg's uncertainty principle leads to a world picture where physical reality is, in many respects, intrinsically undetermined. On the other hand, G\\"odel's incompleteness theorems reveal us the existence of mathematical truths that cannot be demonstrated. More recently, Chaitin has proved that, from the incompleteness theorems, it follows that the random character of a given mathematical sequence cannot be proved in general (it is 'undecidable'). I reflect here on the consequences derived from the indeterminacy of the future and the undecidability of randomness, concluding that the question of the presence or absence of finality in nature is fundamentally outside the scope of the scientific method.
Sibling Dependence, Uncertainty and Education
DEFF Research Database (Denmark)
Lilleør, Helene Bie
investigates the effects of future income uncertainty on sibling dependence in the schooling decisions of rural households in developing countries. Schooling tends to direct skills towards future urban employment, whereas traditional rural education or on-farm learning-by-doing tends to direct skills towards...... to a three period setting. This allows me to explore the natural sequentiality in the schooling decision of older and younger siblings. The model can generate testable empirical implications, which can be taken to any standard cross-sectional data set. I find empirical evidence of negative sibling dependence...... in the educational decision, which is consistent with a human capital portfolio theory of risk diversification and which cannot be explained by sibling rivalry over scarce resources for credit constrained households. The paper thus provides a complementary explanation to why enrolment rates in developing countries...
Social Preferences and Strategic Uncertainty
DEFF Research Database (Denmark)
Cabrales, Antonio; Miniaci, Raffaele; Piovesan, Marco;
This paper reports experimental evidence on a stylized labor market. The experiment is designed as a sequence of three phases. In the first two phases, P1 and P2; agents face simple games, which we use to estimate subjects' social and reciprocity concerns, together with their beliefs. In the last...... phase, P3; four principals, who face four teams of two agents, compete by offering agents a contract from a fixed menu. Then, each agent selects one of the available contracts (i.e. he "chooses to work" for a principal). Production is determined by the outcome of a simple effort game induced...... by the chosen contract. We find that (heterogeneous) social preferences are significant determinants of choices in all phases of the experiment. Since the available contracts display a trade-off between fairness and strategic uncertainty, we observe that the latter is a much stronger determinant of choices...
Attitudes, beliefs, uncertainty and risk
Energy Technology Data Exchange (ETDEWEB)
Greenhalgh, Geoffrey [Down Park Place, Crawley Down (United Kingdom)
2001-07-01
There is now unmistakable evidence of a widening split within the Western industrial nations arising from conflicting views of society; for and against change. The argument is over the benefits of 'progress' and growth. On one side are those who seek more jobs, more production and consumption, higher standards of living, an ever-increasing GNP with an increasing globalisation of production and welcome the advances of science and technology confident that any temporary problems that arise can be solved by further technological development - possible energy shortages as a growing population increases energy usage can be met by nuclear power development; food shortages by the increased yields of GM crops. In opposition are those who put the quality of life before GNP, advocate a more frugal life-style, reducing needs and energy consumption, and, pointing to the harm caused by increasing pollution, press for cleaner air and water standards. They seek to reduce the pressure of an ever-increasing population and above all to preserve the natural environment. This view is associated with a growing uncertainty as the established order is challenged with the rise in status of 'alternative' science and medicine. This paper argues that these conflicting views reflect instinctive attitudes. These in turn draw support from beliefs selected from those which uncertainty offers. Where there is scope for argument over the truth or validity of a 'fact', the choice of which of the disputed views to believe will be determined by a value judgement. This applies to all controversial social and political issues. Nuclear waste disposal and biotechnology are but two particular examples in the technological field; joining the EMU is a current political controversy where value judgements based on attitudes determine beliefs. When, or if, a controversy is finally resolved the judgement arrived at will be justified by the belief that the consequences of the course
Marine reserves with ecological uncertainty.
Grafton, R Quentin; Kompas, Tom; Lindenmayer, David
2005-09-01
To help manage the fluctuations inherent in fish populations scientists have argued for both an ecosystem approach to management and the greater use of marine reserves. Support for reserves includes empirical evidence that they can raise the spawning biomass and mean size of exploited populations, increase the abundance of species and, relative to reference sites, raise population density, biomass, fish size and diversity. By contrast, fishers often oppose the establishment and expansion of marine reserves and claim that reserves provide few, if any, economic payoffs. Using a stochastic optimal control model with two forms of ecological uncertainty we demonstrate that reserves create a resilience effect that allows for the population to recover faster, and can also raise the harvest immediately following a negative shock. The tradeoff of a larger reserve is a reduced harvest in the absence of a negative shock such that a reserve will never encompass the entire population if the goal is to maximize the economic returns from harvesting, and fishing is profitable. Under a wide range of parameter values with ecological uncertainty, and in the 'worst case' scenario for a reserve, we show that a marine reserve can increase the economic payoff to fishers even when the harvested population is not initially overexploited, harvesting is economically optimal and the population is persistent. Moreover, we show that the benefits of a reserve cannot be achieved by existing effort or output controls. Our results demonstrate that, in many cases, there is no tradeoff between the economic payoff of fishers and ecological benefits when a reserve is established at equal to, or less than, its optimum size.
Directory of Open Access Journals (Sweden)
W. M. Angevine
2014-07-01
Full Text Available Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF model drive otherwise identical simulations with FLEXPART for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30–40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15–20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.
Angevine, W. M.; Brioude, J.; McKeen, S.; Holloway, J. S.
2014-12-01
Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and it has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART-WRF for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30-40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15-20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.
Multiplatform application for calculating a combined standard uncertainty using a Monte Carlo method
Niewinski, Marek; Gurnecki, Pawel
2016-12-01
The paper presents a new computer program for calculating a combined standard uncertainty. It implements the algorithm described in JCGM 101:20081 which is concerned with the use of a Monte Carlo method as an implementation of the propagation of distributions for uncertainty evaluation. The accuracy of the calculation has been obtained by using the high quality random number generators. The paper describes the main principles of the program and compares the obtained result with example problems presented in JCGM Supplement 1.
Present theoretical uncertainties on charm hadroproduction in QCD and prompt neutrino fluxes
Directory of Open Access Journals (Sweden)
Garzelli M.V.
2016-01-01
Full Text Available Prompt neutrino fluxes are basic backgrounds in the search of high-energy neutrinos of astrophysical origin, performed by means of full-size neutrino telescopes located at Earth, under ice or under water. Predictions for these fluxes are provided on the basis of up-to-date theoretical results for charm hadroproduction in perturbative QCD, together with a comprehensive discussion of the various sources of theoretical uncertainty affecting their computation, and a quantitative estimate of each uncertainty contribution.
Low cost high performance uncertainty quantification
Bekas, C.
2009-01-01
Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications. Copyright © 2009 ACM.
Unscented transform-based uncertainty analysis of rotating coil transducers for field mapping
Arpaia, P.; De Matteis, E.; Schiano Lo Moriello, R.
2016-03-01
The uncertainty of a rotating coil transducer for magnetic field mapping is analyzed. Unscented transform and statistical design of experiments are combined to determine magnetic field expectation, standard uncertainty, and separate contributions of the uncertainty sources. For nonlinear measurement models, the unscented transform-based approach is more error-proof than the linearization underlying the "Guide to the expression of Uncertainty in Measurements" (GUMs), owing to the absence of model approximations and derivatives computation. When GUM assumptions are not met, the deterministic sampling strategy strongly reduces computational burden with respect to Monte Carlo-based methods proposed by the Supplement 1 of the GUM. Furthermore, the design of experiments and the associated statistical analysis allow the uncertainty sources domain to be explored efficiently, as well as their significance and single contributions to be assessed for an effective setup configuration. A straightforward experimental case study highlights that a one-order-of-magnitude reduction in the relative uncertainty of the coil area produces a decrease in uncertainty of the field mapping transducer by a factor of 25 with respect to the worst condition. Moreover, about 700 trials and the related processing achieve results corresponding to 5 × 106 brute-force Monte Carlo simulations.
A Two-Step Approach to Uncertainty Quantification of Core Simulators
Directory of Open Access Journals (Sweden)
Artem Yankov
2012-01-01
Full Text Available For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor and in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.
Dakota uncertainty quantification methods applied to the NEK-5000 SAHEX model.
Energy Technology Data Exchange (ETDEWEB)
Weirs, V. Gregory
2014-03-01
This report summarizes the results of a NEAMS project focused on the use of uncertainty and sensitivity analysis methods within the NEK-5000 and Dakota software framework for assessing failure probabilities as part of probabilistic risk assessment. NEK-5000 is a software tool under development at Argonne National Laboratory to perform computational fluid dynamics calculations for applications such as thermohydraulics of nuclear reactor cores. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. The goal of this work is to demonstrate the use of uncertainty quantification methods in Dakota with NEK-5000.
Assessment of SFR Wire Wrap Simulation Uncertainties
Energy Technology Data Exchange (ETDEWEB)
Delchini, Marc-Olivier G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Popov, Emilian L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Pointer, William David [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Swiler, Laura P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-09-30
Predictive modeling and simulation of nuclear reactor performance and fuel are challenging due to the large number of coupled physical phenomena that must be addressed. Models that will be used for design or operational decisions must be analyzed for uncertainty to ascertain impacts to safety or performance. Rigorous, structured uncertainty analyses are performed by characterizing the model’s input uncertainties and then propagating the uncertainties through the model to estimate output uncertainty. This project is part of the ongoing effort to assess modeling uncertainty in Nek5000 simulations of flow configurations relevant to the advanced reactor applications of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. Three geometries are under investigation in these preliminary assessments: a 3-D pipe, a 3-D 7-pin bundle, and a single pin from the Thermal-Hydraulic Out-of-Reactor Safety (THORS) facility.
Uncertainty in tsunami sediment transport modeling
Jaffe, Bruce E.; Goto, Kazuhisa; Sugawara, Daisuke; Gelfenbaum, Guy R.; La Selle, SeanPaul M.
2016-01-01
Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. We explore sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami, study site, available input data, sediment grain size, and model. Although uncertainty has the potential to be large, published case studies indicate that both forward and inverse tsunami sediment transport models perform well enough to be useful for deciphering tsunami characteristics, including size, from deposits. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and development of hybrid modeling approaches to exploit the strengths of forward and inverse models.
Quantifying Mixed Uncertainties in Cyber Attacker Payoffs
Energy Technology Data Exchange (ETDEWEB)
Chatterjee, Samrat; Halappanavar, Mahantesh; Tipireddy, Ramakrishna; Oster, Matthew R.; Saha, Sudip
2015-04-15
Representation and propagation of uncertainty in cyber attacker payoffs is a key aspect of security games. Past research has primarily focused on representing the defender’s beliefs about attacker payoffs as point utility estimates. More recently, within the physical security domain, attacker payoff uncertainties have been represented as Uniform and Gaussian probability distributions, and intervals. Within cyber-settings, continuous probability distributions may still be appropriate for addressing statistical (aleatory) uncertainties where the defender may assume that the attacker’s payoffs differ over time. However, systematic (epistemic) uncertainties may exist, where the defender may not have sufficient knowledge or there is insufficient information about the attacker’s payoff generation mechanism. Such epistemic uncertainties are more suitably represented as probability boxes with intervals. In this study, we explore the mathematical treatment of such mixed payoff uncertainties.
Bayesian Uncertainty Analyses Via Deterministic Model
Krzysztofowicz, R.
2001-05-01
Rational decision-making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Suppose the state-of-knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of three Bayesian approaches to producing a probability distribution of the predictand via any deterministic model. The Bayesian Processor of Output (BPO) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Processor of Ensemble (BPE) quantifies the total uncertainty in terms of a posterior distribution, conditional on an ensemble of model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution.
Visual Semiotics & Uncertainty Visualization: An Empirical Study.
MacEachren, A M; Roth, R E; O'Brien, J; Li, B; Swingley, D; Gahegan, M
2012-12-01
This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results.
Incorporating Forecast Uncertainty in Utility Control Center
Energy Technology Data Exchange (ETDEWEB)
Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian
2014-07-09
Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)
A computer scientist looks at game theory
Halpern, Joseph Y.
2002-01-01
I consider issues in distributed computation that should be of relevance to game theory. In particular, I focus on (a) representing knowledge and uncertainty, (b) dealing with failures, and (c) specification of mechanisms.
Directory of Open Access Journals (Sweden)
K. S. C. Freeman
Full Text Available The discussion in the preceding paper is restricted to the uncertainties in magnetic-field-line tracing in the magnetosphere resulting from published standard errors in the spherical harmonic coefficients that define the axisymmetric part of the internal geomagnetic field (i.e. gn0 ± δgn0. Numerical estimates of these uncertainties based on an analytic equation for axisymmetric field lines are in excellent agreement with independent computational estimates based on stepwise numerical integration along magnetic field lines. This comparison confirms the accuracy of the computer program used in the present paper to estimate the uncertainties in magnetic-field-line tracing that arise from published standard errors in the full set of spherical harmonic coefficients, which define the complete (non-axisymmetric internal geomagnetic field (i.e. gnm ± δgnm and hnm ± δhnm. An algorithm is formulated that greatly reduces the computing time required to estimate these uncertainties in magnetic-field-line tracing. The validity of this algorithm is checked numerically for both the axisymmetric part of the internal geomagnetic field in the general case (1 ≤ n ≤ 10 and the complete internal geomagnetic field in a restrictive case (0 ≤ m ≤ n, 1 ≤ n ≤ 3. On this basis it is assumed that the algorithm can be used with confidence in those cases for which the computing time would otherwise be prohibitively long. For the complete internal geomagnetic field, the maximum characteristic uncertainty in the geocentric distance of a field line that crosses the geomagnetic equator at a nominal dipolar distance of 2 RE is typically 100 km. The corresponding characteristic uncertainty for a field line that crosses the geomagnetic equator at a nominal dipolar distance of 6 RE is typically 500 km. Histograms and scatter plots showing the characteristic uncertainties associated with magnetic-field-line tracing in the magnetosphere are presented for a range of
Uncertainty quantification for proton-proton fusion in chiral effective field theory
Acharya, B.; Carlsson, B. D.; Ekström, A.; Forssén, C.; Platter, L.
2016-09-01
We compute the S-factor of the proton-proton (pp) fusion reaction using chiral effective field theory (χEFT) up to next-to-next-to-leading order (NNLO) and perform a rigorous uncertainty analysis of the results. We quantify the uncertainties due to (i) the computational method used to compute the pp cross section in momentum space, (ii) the statistical uncertainties in the low-energy coupling constants of χEFT, (iii) the systematic uncertainty due to the χEFT cutoff, and (iv) systematic variations in the database used to calibrate the nucleon-nucleon interaction. We also examine the robustness of the polynomial extrapolation procedure, which is commonly used to extract the threshold S-factor and its energy-derivatives. By performing a statistical analysis of the polynomial fit of the energy-dependent S-factor at several different energy intervals, we eliminate a systematic uncertainty that can arise from the choice of the fit interval in our calculations. In addition, we explore the statistical correlations between the S-factor and few-nucleon observables such as the binding energies and point-proton radii of 2,3H and 3He as well as the D-state probability and quadrupole moment of 2H, and the β-decay of 3H. We find that, with the state-of-the-art optimization of the nuclear Hamiltonian, the statistical uncertainty in the threshold S-factor cannot be reduced beyond 0.7%.
Research of relationship between uncertainty and investment
Institute of Scientific and Technical Information of China (English)
MENG Li; WANG Ding-wei
2005-01-01
This study focuses on revealing the relationship between uncertainty and investment probability through real option model involving investment critical trigger and project earning. Use of Matlab software on the experimental results showing that project earning volatility influences investment probability, led the authors to conclude that this notion is not always correct, as increasing uncertainty should have an inhibiting effect on investment, and that in certain situation, increasing uncertainty actually increases the investment probability and so, should have positive impact on investment.
Whitepaper on Uncertainty Quantification for MPACT
Energy Technology Data Exchange (ETDEWEB)
Williams, Mark L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-12-17
The MPACT code provides the ability to perform high-fidelity deterministic calculations to obtain a wide variety of detailed results for very complex reactor core models. However MPACT currently does not have the capability to propagate the effects of input data uncertainties to provide uncertainties in the calculated results. This white paper discusses a potential method for MPACT uncertainty quantification (UQ) based on stochastic sampling.
Measures of uncertainty in power split systems
Özdemir, Serhan
2007-01-01
This paper discusses the overlooked uncertainty inherent in every transmission. The uncertainty aspect has been often, for the sake of clarity, ignored. Instead, mechanical transmissions have been characterized traditionally by their transmission efficacies. It is known that transmission localities are sources of power loss, depending on many factors, hence sources of uncertainty. Thus each transmission of power should not only be designated by a constant of efficiency but also by an expressi...
Uncertainty propagation with functionally correlated quantities
Giordano, Mosè
2016-01-01
Many uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities. In this paper we review one strategy to deal with uncertainty propagation of quantities that are functionally correlated, and introduce a new software offering this feature: the Julia package Measurements.jl. It supports real and complex numbers with uncertainty, arbitrary-precision calculations, mathematical and linear algebra operations with matrices and arrays.
Vibration and stress analysis in the presence of structural uncertainty
Energy Technology Data Exchange (ETDEWEB)
Langley, R S, E-mail: RSL21@eng.cam.ac.u [Department of Engineering, University of Cambridge, Cambridge CB2 1PZ (United Kingdom)
2009-08-01
At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular ''non-parametric'' methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies.
An approximation approach for uncertainty quantification using evidence theory
Energy Technology Data Exchange (ETDEWEB)
Bae, Ha-Rok; Grandhi, Ramana V.; Canfield, Robert A
2004-12-01
Over the last two decades, uncertainty quantification (UQ) in engineering systems has been performed by the popular framework of probability theory. However, many scientific and engineering communities realize that there are limitations in using only one framework for quantifying the uncertainty experienced in engineering applications. Recently evidence theory, also called Dempster-Shafer theory, was proposed to handle limited and imprecise data situations as an alternative to the classical probability theory. Adaptation of this theory for large-scale engineering structures is a challenge due to implicit nature of simulations and excessive computational costs. In this work, an approximation approach is developed to improve the practical utility of evidence theory in UQ analysis. The techniques are demonstrated on composite material structures and airframe wing aeroelastic design problem.
Vibration and stress analysis in the presence of structural uncertainty
Langley, R. S.
2009-08-01
At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular "non-parametric" methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies.
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification
Energy Technology Data Exchange (ETDEWEB)
Li, Chunyuan; Stevens, Andrew J.; Chen, Changyou; Pu, Yunchen; Gan, Zhe; Carin, Lawrence
2016-08-10
Learning the representation of shape cues in 2D & 3D objects for recognition is a fundamental task in computer vision. Deep neural networks (DNNs) have shown promising performance on this task. Due to the large variability of shapes, accurate recognition relies on good estimates of model uncertainty, ignored in traditional training of DNNs, typically learned via stochastic optimization. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (SG-MCMC) to learn weight uncertainty in DNNs. It yields principled Bayesian interpretations for the commonly used Dropout/DropConnect techniques and incorporates them into the SG-MCMC framework. Extensive experiments on 2D & 3D shape datasets and various DNN models demonstrate the superiority of the proposed approach over stochastic optimization. Our approach yields higher recognition accuracy when used in conjunction with Dropout and Batch-Normalization.
Dealing with uncertainties in angles-only initial orbit determination
Armellin, Roberto; Di Lizia, Pierluigi; Zanetti, Renato
2016-08-01
A method to deal with uncertainties in initial orbit determination (IOD) is presented. This is based on the use of Taylor differential algebra (DA) to nonlinearly map uncertainties from the observation space to the state space. When a minimum set of observations is available, DA is used to expand the solution of the IOD problem in Taylor series with respect to measurement errors. When more observations are available, high order inversion tools are exploited to obtain full state pseudo-observations at a common epoch. The mean and covariance of these pseudo-observations are nonlinearly computed by evaluating the expectation of high order Taylor polynomials. Finally, a linear scheme is employed to update the current knowledge of the orbit. Angles-only observations are considered and simplified Keplerian dynamics adopted to ease the explanation. Three test cases of orbit determination of artificial satellites in different orbital regimes are presented to discuss the feature and performances of the proposed methodology.
On the Measurement of Randomness (Uncertainty: A More Informative Entropy
Directory of Open Access Journals (Sweden)
Tarald O. Kvålseth
2016-04-01
Full Text Available As a measure of randomness or uncertainty, the Boltzmann–Shannon entropy H has become one of the most widely used summary measures of a variety of attributes (characteristics in different disciplines. This paper points out an often overlooked limitation of H: comparisons between differences in H-values are not valid. An alternative entropy H K is introduced as a preferred member of a new family of entropies for which difference comparisons are proved to be valid by satisfying a given value-validity condition. The H K is shown to have the appropriate properties for a randomness (uncertainty measure, including a close linear relationship to a measurement criterion based on the Euclidean distance between probability distributions. This last point is demonstrated by means of computer generated random distributions. The results are also compared with those of another member of the entropy family. A statistical inference procedure for the entropy H K is formulated.
Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods
Davis, A. D.
2015-12-01
The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity
Uncertainty propagation within the UNEDF models
Haverinen, T
2016-01-01
The parameters of the nuclear energy density have to be adjusted to experimental data. As a result they carry certain uncertainty which then propagates to calculated values of observables. In the present work we quantify the statistical uncertainties on binding energies for three UNEDF Skyrme energy density functionals by taking advantage of the knowledge of the model parameter uncertainties. We find that the uncertainty of UNEDF models increases rapidly when going towards proton or neutron rich nuclei. We also investigate the impact of each model parameter on the total error budget.
Uncertainty and the ethics of clinical trials.
Hansson, Sven Ove
2006-01-01
A probabilistic explication is offered of equipoise and uncertainty in clinical trials. In order to be useful in the justification of clinical trials, equipoise has to be interpreted in terms of overlapping probability distributions of possible treatment outcomes, rather than point estimates representing expectation values. Uncertainty about treatment outcomes is shown to be a necessary but insufficient condition for the ethical defensibility of clinical trials. Additional requirements are proposed for the nature of that uncertainty. The indecisiveness of our criteria for cautious decision-making under uncertainty creates the leeway that makes clinical trials defensible.
Uncertainty relations for general unitary operators
Bagchi, Shrobona; Pati, Arun Kumar
2016-10-01
We derive several uncertainty relations for two arbitrary unitary operators acting on physical states of a Hilbert space. We show that our bounds are tighter in various cases than the ones existing in the current literature. Using the uncertainty relation for the unitary operators, we obtain the tight state-independent lower bound for the uncertainty of two Pauli observables and anticommuting observables in higher dimensions. With regard to the minimum-uncertainty states, we derive the minimum-uncertainty state equation by the analytic method and relate this to the ground-state problem of the Harper Hamiltonian. Furthermore, the higher-dimensional limit of the uncertainty relations and minimum-uncertainty states are explored. From an operational point of view, we show that the uncertainty in the unitary operator is directly related to the visibility of quantum interference in an interferometer where one arm of the interferometer is affected by a unitary operator. This shows a principle of preparation uncertainty, i.e., for any quantum system, the amount of visibility for two general noncommuting unitary operators is nontrivially upper bounded.
Uncertainty propagation within the UNEDF models
Haverinen, T.; Kortelainen, M.
2017-04-01
The parameters of the nuclear energy density have to be adjusted to experimental data. As a result they carry certain uncertainty which then propagates to calculated values of observables. In the present work we quantify the statistical uncertainties of binding energies, proton quadrupole moments and proton matter radius for three UNEDF Skyrme energy density functionals by taking advantage of the knowledge of the model parameter uncertainties. We find that the uncertainty of UNEDF models increases rapidly when going towards proton or neutron rich nuclei. We also investigate the impact of each model parameter on the total error budget.
Towards a different attitude to uncertainty
Directory of Open Access Journals (Sweden)
Guy Pe'er
2014-10-01
Full Text Available The ecological literature deals with uncertainty primarily from the perspective of how to reduce it to acceptable levels. However, the current rapid and ubiquitous environmental changes, as well as anticipated rates of change, pose novel conditions and complex dynamics due to which many sources of uncertainty are difficult or even impossible to reduce. These include both uncertainty in knowledge (epistemic uncertainty and societal responses to it. Under these conditions, an increasing number of studies ask how one can deal with uncertainty as it is. Here, we explore the question how to adopt an overall alternative attitude to uncertainty, which accepts or even embraces it. First, we show that seeking to reduce uncertainty may be counterproductive under some circumstances. It may yield overconfidence, ignoring early warning signs, policy- and societal stagnation, or irresponsible behaviour if personal certainty is offered by externalization of environmental costs. We then demonstrate that uncertainty can have positive impacts by driving improvements in knowledge, promoting cautious action, contributing to keeping societies flexible and adaptable, enhancing awareness, support and involvement of the public in nature conservation, and enhancing cooperation and communication. We discuss the risks of employing a certainty paradigm on uncertain knowledge, the potential benefits of adopting an alternative attitude to uncertainty, and the need to implement such an attitude across scales – from adaptive management at the local scale, to the evolving Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES at the global level.
Wastewater treatment modelling: dealing with uncertainties
DEFF Research Database (Denmark)
Belia, E.; Amerlinck, Y.; Benedetti, L.;
2009-01-01
This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation...... of a wastewater treatment system. It briefly references the methods currently used to evaluate prediction accuracy and uncertainty and discusses the relevance of uncertainty evaluations in model applications. The paper aims to raise awareness and initiate a comprehensive discussion among professionals on model...
Force calibration using errors-in-variables regression and Monte Carlo uncertainty evaluation
Bartel, Thomas; Stoudt, Sara; Possolo, Antonio
2016-06-01
An errors-in-variables regression method is presented as an alternative to the ordinary least-squares regression computation currently employed for determining the calibration function for force measuring instruments from data acquired during calibration. A Monte Carlo uncertainty evaluation for the errors-in-variables regression is also presented. The corresponding function (which we call measurement function, often called analysis function in gas metrology) necessary for the subsequent use of the calibrated device to measure force, and the associated uncertainty evaluation, are also derived from the calibration results. Comparisons are made, using real force calibration data, between the results from the errors-in-variables and ordinary least-squares analyses, as well as between the Monte Carlo uncertainty assessment and the conventional uncertainty propagation employed at the National Institute of Standards and Technology (NIST). The results show that the errors-in-variables analysis properly accounts for the uncertainty in the applied calibrated forces, and that the Monte Carlo method, owing to its intrinsic ability to model uncertainty contributions accurately, yields a better representation of the calibration uncertainty throughout the transducer’s force range than the methods currently in use. These improvements notwithstanding, the differences between the results produced by the current and by the proposed new methods generally are small because the relative uncertainties of the inputs are small and most contemporary load cells respond approximately linearly to such inputs. For this reason, there will be no compelling need to revise any of the force calibration reports previously issued by NIST.
Uncertainty characterization in the retrieval of an atmospheric point release
Singh, Sarvesh Kumar; Kumar, Pramod; Turbelin, Grégory; Rani, Raj
2017-03-01
The study proposes a methodology in a recent inversion technique, called as Renormalization, to characterize the uncertainties in the reconstruction of a point source. The estimates are derived for measuring the inversion error, the degree of model fit towards measurements (model determination coefficient) and the confidence intervals for the retrieved point source parameters (mainly, location and strength). The inversion error is reflected through an angular estimate which measures the deviation between the measured and predicted concentrations. The uncertainty estimation methodology is evaluated for point source reconstruction studies, using real measurements from two field experiments, known as Fusion Field Trials 2007 (FFT07) in flat terrain and Mock Urban Setting Test (MUST) in urban like terrain. In FFT07 and MUST experiments, the point source location is retrieved with an average Euclidean distance of 22 m and 15 m respectively. The source strength is retrieved, on average, within a factor of 1.5 in both the datasets. The inversion error is observed as 24o and 21o in FFT07 and MUST experiment, respectively. The 95% confidence interval estimates show that the uncertainty in the retrieved parameters is relatively large in approximately 50% FFT07 and 30% MUST trials in spite of their closeness towards true source parameters. For a comparative analysis, the interval estimates are also compared with a more general method of uncertainty estimation, Residual Bootstrap Sampling. In most of the trials, we observed that the intervals estimates with the present method are comparable (within 10-20% variations) to bootstrap estimates. The proposed methodology provides near accurate and computationally efficient uncertainty estimates in comparison to the methods based on Hessian and sampling procedures.
Final Report. Analysis and Reduction of Complex Networks Under Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Coles, T. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Spantini, A. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Tosatto, L. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
2013-09-30
The project was a collaborative effort among MIT, Sandia National Laboratories (local PI Dr. Habib Najm), the University of Southern California (local PI Prof. Roger Ghanem), and The Johns Hopkins University (local PI Prof. Omar Knio, now at Duke University). Our focus was the analysis and reduction of large-scale dynamical systems emerging from networks of interacting components. Such networks underlie myriad natural and engineered systems. Examples important to DOE include chemical models of energy conversion processes, and elements of national infrastructure—e.g., electric power grids. Time scales in chemical systems span orders of magnitude, while infrastructure networks feature both local and long-distance connectivity, with associated clusters of time scales. These systems also blend continuous and discrete behavior; examples include saturation phenomena in surface chemistry and catalysis, and switching in electrical networks. Reducing size and stiffness is essential to tractable and predictive simulation of these systems. Computational singular perturbation (CSP) has been effectively used to identify and decouple dynamics at disparate time scales in chemical systems, allowing reduction of model complexity and stiffness. In realistic settings, however, model reduction must contend with uncertainties, which are often greatest in large-scale systems most in need of reduction. Uncertainty is not limited to parameters; one must also address structural uncertainties—e.g., whether a link is present in a network—and the impact of random perturbations, e.g., fluctuating loads or sources. Research under this project developed new methods for the analysis and reduction of complex multiscale networks under uncertainty, by combining computational singular perturbation (CSP) with probabilistic uncertainty quantification. CSP yields asymptotic approximations of reduceddimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing
DEFF Research Database (Denmark)
Yagüe-Fabra, J.A.; Ontiveros, S.; Jiménez, R.
2013-01-01
Many factors influence the measurement uncertainty when using computed tomography for dimensional metrology applications. One of the most critical steps is the surface extraction phase. An incorrect determination of the surface may significantly increase the measurement uncertainty. This paper pr...
Flood risk assessment and associated uncertainty
Directory of Open Access Journals (Sweden)
H. Apel
2004-01-01
Full Text Available Flood disaster mitigation strategies should be based on a comprehensive assessment of the flood risk combined with a thorough investigation of the uncertainties associated with the risk assessment procedure. Within the 'German Research Network of Natural Disasters' (DFNK the working group 'Flood Risk Analysis' investigated the flood process chain from precipitation, runoff generation and concentration in the catchment, flood routing in the river network, possible failure of flood protection measures, inundation to economic damage. The working group represented each of these processes by deterministic, spatially distributed models at different scales. While these models provide the necessary understanding of the flood process chain, they are not suitable for risk and uncertainty analyses due to their complex nature and high CPU-time demand. We have therefore developed a stochastic flood risk model consisting of simplified model components associated with the components of the process chain. We parameterised these model components based on the results of the complex deterministic models and used them for the risk and uncertainty analysis in a Monte Carlo framework. The Monte Carlo framework is hierarchically structured in two layers representing two different sources of uncertainty, aleatory uncertainty (due to natural and anthropogenic variability and epistemic uncertainty (due to incomplete knowledge of the system. The model allows us to calculate probabilities of occurrence for events of different magnitudes along with the expected economic damage in a target area in the first layer of the Monte Carlo framework, i.e. to assess the economic risks, and to derive uncertainty bounds associated with these risks in the second layer. It is also possible to identify the contributions of individual sources of uncertainty to the overall uncertainty. It could be shown that the uncertainty caused by epistemic sources significantly alters the results
A new algorithm for importance analysis of the inputs with distribution parameter uncertainty
Li, Luyi; Lu, Zhenzhou
2016-10-01
Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo (MC) sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a 'surrogate sampling probability density function (SS-PDF)' and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.
Handbook of management under uncertainty
2001-01-01
A mere few years ago it would have seemed odd to propose a Handbook on the treatment of management problems within a sphere of uncertainty. Even today, on the threshold of the third millennium, this statement may provoke a certain wariness. In fact, to resort to exact or random data, that is probable date, is quite normal and con venient, as we then know where we are going best, where we are proposing to go if all occurs as it is conceived and hoped for. To treat uncertain information, to accept a new principle and from there determined criteria, without being sure of oneself and confiding only in the will to better understand objects and phenomena, constitutes and compromise with a new form of understanding the behaviour of current beings that goes even further than simple rationality. Economic Science and particularly the use of its elements of configuration in the world of management, has imbued several generations with an analytical spirit that has given rise to the elaboration of theories widely accept...
On the dominant uncertainty source of climate change projections at the local scale
Fatichi, Simone; Ivanov, Valeriy; Paschalis, Athanasios; Molnar, Peter; Rimkus, Stefan; Kim, Jongho; Peleg, Nadav; Burlando, Paolo; Caporali, Enrica
2016-04-01
Decision makers and stakeholders are usually concerned about climate change projections at local spatial scales and fine temporal resolutions. This contrasts with the reliability of climate models, which is typically higher at the global and regional scales, Therefore, there is a demand for advanced methodologies that offer the capability of transferring predictions of climate models and relative uncertainty to scales commensurate with practical applications and for higher order statistics (e.g., few square kilometres and sub-daily scale). A stochastic downscaling technique that makes use of an hourly weather generator (AWE-GEN) and of a Bayesian methodology to weight realizations from different climate models is used to generate local scale meteorological time series of plausible "futures". We computed factors of change from realizations of 32 climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and for different emission scenarios (RCP 4.5 and RCP 8.5). Future climate projections for several meteorological variables (precipitation, air temperature, relative humidity, shortwave radiation) are simulated at three locations characterized by remarkably different climates, Zurich (Switzlerand), Miami and San Francisco (USA). The methodology is designed to partition three main sources of uncertainty: uncertainty due to climate models (model epistemic uncertainty), anthropogenic forcings (scenario uncertainty), and internal climate variability (stochastic uncertainty). The three types of uncertainty sources are considered as dependent, implicitly accounting for possible co-variances among the sources. For air temperature, the magnitude of the different uncertainty sources is comparable for mid-of-the-century projections, while scenario uncertainty dominates at large lead-times. The dominant source of uncertainty for changes in precipitation mean and extremes is internal climate variability, which is accounting for more than 80% of the total
Indian Academy of Sciences (India)
Hesheng Tang; Yu Su; Jiao Wang
2015-08-01
The paper describes a procedure for the uncertainty quantification (UQ) using evidence theory in buckling analysis of semi-rigid jointed frame structures under mixed epistemic–aleatory uncertainty. The design uncertainties (geometrical, material, strength, and manufacturing) are often prevalent in engineering applications. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, measurement, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. Unfortunately, propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than propagation of a probabilistic representation for uncertainty. In order to alleviate the computational difficulties in the evidence theory based UQ analysis, a differential evolution-based computational strategy for propagation of epistemic uncertainty in a system with evidence theory is presented here. A UQ analysis for the buckling load of steel-plane frames with semi-rigid connections is given herein to demonstrate accuracy and efficiency of the proposed method.
PIV Uncertainty Methodologies for CFD Code Validation at the MIR Facility
Energy Technology Data Exchange (ETDEWEB)
Piyush Sabharwall; Richard Skifton; Carl Stoots; Eung Soo Kim; Thomas Conder
2013-12-01
Currently, computational fluid dynamics (CFD) is widely used in the nuclear thermal hydraulics field for design and safety analyses. To validate CFD codes, high quality multi dimensional flow field data are essential. The Matched Index of Refraction (MIR) Flow Facility at Idaho National Laboratory has a unique capability to contribute to the development of validated CFD codes through the use of Particle Image Velocimetry (PIV). The significance of the MIR facility is that it permits non intrusive velocity measurement techniques, such as PIV, through complex models without requiring probes and other instrumentation that disturb the flow. At the heart of any PIV calculation is the cross-correlation, which is used to estimate the displacement of particles in some small part of the image over the time span between two images. This image displacement is indicated by the location of the largest peak. In the MIR facility, uncertainty quantification is a challenging task due to the use of optical measurement techniques. Currently, this study is developing a reliable method to analyze uncertainty and sensitivity of the measured data and develop a computer code to automatically analyze the uncertainty/sensitivity of the measured data. The main objective of this study is to develop a well established uncertainty quantification method for the MIR Flow Facility, which consists of many complicated uncertainty factors. In this study, the uncertainty sources are resolved in depth by categorizing them into uncertainties from the MIR flow loop and PIV system (including particle motion, image distortion, and data processing). Then, each uncertainty source is mathematically modeled or adequately defined. Finally, this study will provide a method and procedure to quantify the experimental uncertainty in the MIR Flow Facility with sample test results.
Uncertainty propagation for nonlinear vibrations: A non-intrusive approach
Panunzio, A. M.; Salles, Loic; Schwingshackl, C. W.
2017-02-01
The propagation of uncertain input parameters in a linear dynamic analysis is reasonably well established today, but with the focus of the dynamic analysis shifting towards nonlinear systems, new approaches is required to compute the uncertain nonlinear responses. A combination of stochastic methods (Polynomial Chaos Expansion, PCE) with an Asymptotic Numerical Method (ANM) for the solution of the nonlinear dynamic systems is presented to predict the propagation of random input uncertainties and assess their influence on the nonlinear vibrational behaviour of a system. The proposed method allows the computation of stochastic resonance frequencies and peak amplitudes based on multiple input uncertainties, leading to a series of uncertain nonlinear dynamic responses. One of the main challenges when using the PCE is thereby the Gibbs phenomenon, which can heavily impact the resulting stochastic nonlinear response by introducing spurious oscillations. A novel technique to avoid the Gibbs phenomenon is be presented in this paper, leading to high quality frequency response predictions. A comparison of the proposed stochastic nonlinear analysis technique to traditional Monte Carlo simulations, demonstrates comparable accuracy at a significantly reduced computational cost, thereby validating the proposed approach.
Estimating the uncertainty in underresolved nonlinear dynamics
Energy Technology Data Exchange (ETDEWEB)
Chorin, Alelxandre; Hald, Ole
2013-06-12
The Mori-Zwanzig formalism of statistical mechanics is used to estimate the uncertainty caused by underresolution in the solution of a nonlinear dynamical system. A general approach is outlined and applied to a simple example. The noise term that describes the uncertainty turns out to be neither Markovian nor Gaussian. It is argued that this is the general situation.
Gamma-Ray Telescope and Uncertainty Principle
Shivalingaswamy, T.; Kagali, B. A.
2012-01-01
Heisenberg's Uncertainty Principle is one of the important basic principles of quantum mechanics. In most of the books on quantum mechanics, this uncertainty principle is generally illustrated with the help of a gamma ray microscope, wherein neither the image formation criterion nor the lens properties are taken into account. Thus a better…
Worry, Intolerance of Uncertainty, and Statistics Anxiety
Williams, Amanda S.
2013-01-01
Statistics anxiety is a problem for most graduate students. This study investigates the relationship between intolerance of uncertainty, worry, and statistics anxiety. Intolerance of uncertainty was significantly related to worry, and worry was significantly related to three types of statistics anxiety. Six types of statistics anxiety were…
Return Predictability, Model Uncertainty, and Robust Investment
DEFF Research Database (Denmark)
Lukas, Manuel
Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...
Stochastic variational approach to minimum uncertainty states
Energy Technology Data Exchange (ETDEWEB)
Illuminati, F.; Viola, L. [Dipartimento di Fisica, Padova Univ. (Italy)
1995-05-21
We introduce a new variational characterization of Gaussian diffusion processes as minimum uncertainty states. We then define a variational method constrained by kinematics of diffusions and Schroedinger dynamics to seek states of local minimum uncertainty for general non-harmonic potentials. (author)
Stochastic variational approach to minimum uncertainty states
Illuminati, F; Illuminati, F; Viola, L
1995-01-01
We introduce a new variational characterization of Gaussian diffusion processes as minimum uncertainty states. We then define a variational method constrained by kinematics of diffusions and Schr\\"{o}dinger dynamics to seek states of local minimum uncertainty for general non-harmonic potentials.
Advice under uncertainty in the marine system
Dankel, D.J.; Aps, R.; Padda, G.; Rockmann, C.; Sluijs, van der J.P.; Wilson, D.C.; Degnbol, P.
2012-01-01
There is some uncertainty in the fisheries science–policy interface. Although progress has been made towards more transparency and participation in fisheries science in ICES Areas, routine use of state-of-the-art quantitative and qualitative tools to address uncertainty systematically is still lacki
Advice under uncertainty in the marine system
Dankel, D.J.; Aps, R.; Padda, G.; Rockmann, C.; Sluijs, J.P. van der; Wilson, D.C.; Degnbol, P.
2012-01-01
There is some uncertainty in the fisheries sciencepolicy interface. Although progress has been made towards more transparency and participation in fisheries science in ICES Areas, routine use of state-of-the-art quantitative and qualitative tools to address uncertainty systematically is still lackin
Experimental Uncertainties of TEPC Dose Equivalent
Institute of Scientific and Technical Information of China (English)
ZHANG; Wei-hua; XIAO; Xue-fu; WANG; Zhi-qiang; LIU; Yi-na; LI; Chun-juan; LUO; Hai-long
2013-01-01
The tissue-equivalent proportional counters(TEPC)are widely used for radiation protection in mixed radiation fields.The operational quantity H*(10)can be directly obtained by means of microdosimetric spectra measurements with TEPC.An empirical evaluation of uncertainties is reported in this paper.The sources of uncertainties involved in measuring microdosimetric spectra mainly are the sensitive
Advice under uncertainty in the marine system
DEFF Research Database (Denmark)
Dankel, Dorothy J.; Aps, Robert; Padda, Gurpreet
2012-01-01
There is some uncertainty in the fisheries science–policy interface. Although progress has been made towards more transparency and participation in fisheries science in ICES Areas, routine use of state-of-the-art quantitative and qualitative tools to address uncertainty systematically is still la...
Uncertainty as organizing principle of action
DEFF Research Database (Denmark)
Winther-Lindqvist, Ditte Alexandra
2014-01-01
uncertainty as condition for teenage life when confronted with parental serious illness is presented as the main challenge charracterising this situation. based on 26 semi-structured interviews everyday life with an ill parent is described and analysed. a model of uncertainty is suggested which...
Nonclassicality in phase-number uncertainty relations
Energy Technology Data Exchange (ETDEWEB)
Matia-Hernando, Paloma; Luis, Alfredo [Departamento de Optica, Facultad de Ciencias Fisicas, Universidad Complutense, 28040 Madrid (Spain)
2011-12-15
We show that there are nonclassical states with lesser joint fluctuations of phase and number than any classical state. This is rather paradoxical since one would expect classical coherent states to be always of minimum uncertainty. The same result is obtained when we replace phase by a phase-dependent field quadrature. Number and phase uncertainties are assessed using variance and Holevo relation.
Framework for managing uncertainty in property projects
Reymen, Isabelle M.M.J.; Dewulf, Geert P.M.R.; Blokpoel, Sjoerd B.
2008-01-01
A primary task of property development (or real estate development, RED) is making assessments and managing risks and uncertainties. Property managers cope with a wide range of uncertainties, particularly in the early project phases. Although the existing literature addresses the management of calcu
Uncertainty Quantification with Applications to Engineering Problems
DEFF Research Database (Denmark)
Bigoni, Daniele
The systematic quantification of the uncertainties affecting dynamical systems and the characterization of the uncertainty of their outcomes is critical for engineering design and analysis, where risks must be reduced as much as possible. Uncertainties stem naturally from our limitations in measu......The systematic quantification of the uncertainties affecting dynamical systems and the characterization of the uncertainty of their outcomes is critical for engineering design and analysis, where risks must be reduced as much as possible. Uncertainties stem naturally from our limitations...... in measurements, predictions and manufacturing, and we can say that any dynamical system used in engineering is subject to some of these uncertainties. The first part of this work presents an overview of the mathematical framework used in Uncertainty Quantification (UQ) analysis and introduces the spectral tensor...... some auxiliary properties, we will apply PC on it, obtaining the STT-decomposition. This will allow the decoupling of each dimension, leading to a much cheaper construction of the PC surrogate. In the associated paper, the capabilities of the STT-decomposition are checked on commonly used test...
Energy Technology Data Exchange (ETDEWEB)
Knoerr, Wofram [ifeu - Institut fuer Energie- und Umweltforschung Heidelberg gGmbH, Heidelberg (Germany); Heldstab, Juerg; Kasser, Florian; Keller, Mario [INFRAS, Zuerich (Switzerland)
2010-05-15
Germany is obligated to the implementation of emission inventories for climatic gases and air pollutants. This requires the proof and documentation of the data quality and uncertainties. In the project under consideration the uncertainties of the ZSE data records (ZSE = central system emissions) of the source groups road traffic, rail traffic, inland navigation traffic and residual traffic of the emission inventory are to be determined for all fuel methods, greenhouse gases and air pollutants. The basis of the ZSE data records are the programs TREMOD and TREMOD MM as well as the sales figures of fuels. Provisional results of the actual mineral oil statistics, updated road performances and stocks of vehicles as well as the actual emission factor data base of traffic are used as a fundament for the determination of uncertainties.
Multicandidate Elections: Aggregate Uncertainty in the Laboratory*
Bouton, Laurent; Castanheira, Micael; Llorente-Saguer, Aniol
2015-01-01
The rational-voter model is often criticized on the grounds that two of its central predictions (the paradox of voting and Duverger’s Law) are at odds with reality. Recent theoretical advances suggest that these empirically unsound predictions might be an artifact of an (arguably unrealistic) assumption: the absence of aggregate uncertainty about the distribution of preferences in the electorate. In this paper, we propose direct empirical evidence of the effect of aggregate uncertainty in multicandidate elections. Adopting a theory-based experimental approach, we explore whether aggregate uncertainty indeed favors the emergence of non-Duverger’s law equilibria in plurality elections. Our experimental results support the main theoretical predictions: sincere voting is a predominant strategy under aggregate uncertainty, whereas without aggregate uncertainty, voters massively coordinate their votes behind one candidate, who wins almost surely.
Quantifying uncertainty in future ocean carbon uptake
Dunne, John P.
2016-10-01
Attributing uncertainty in ocean carbon uptake between societal trajectory (scenarios), Earth System Model construction (structure), and inherent natural variation in climate (internal) is critical to make progress in identifying, understanding, and reducing those uncertainties. In the present issue of Global Biogeochemical Cycles, Lovenduski et al. (2016) disentangle these drivers of uncertainty in ocean carbon uptake over time and space and assess the resulting implications for the emergence timescales of structural and scenario uncertainty over internal variability. Such efforts are critical for establishing realizable and efficient monitoring goals and prioritizing areas of continued model development. Under recently proposed climate stabilization targets, such efforts to partition uncertainty also become increasingly critical to societal decision-making in the context of carbon stabilization.
Uncertainty Analysis for Photovoltaic Degradation Rates (Poster)
Energy Technology Data Exchange (ETDEWEB)
Jordan, D.; Kurtz, S.; Hansen, C.
2014-04-01
Dependable and predictable energy production is the key to the long-term success of the PV industry. PV systems show over the lifetime of their exposure a gradual decline that depends on many different factors such as module technology, module type, mounting configuration, climate etc. When degradation rates are determined from continuous data the statistical uncertainty is easily calculated from the regression coefficients. However, total uncertainty that includes measurement uncertainty and instrumentation drift is far more difficult to determine. A Monte Carlo simulation approach was chosen to investigate a comprehensive uncertainty analysis. The most important effect for degradation rates is to avoid instrumentation that changes over time in the field. For instance, a drifting irradiance sensor, which can be achieved through regular calibration, can lead to a substantially erroneous degradation rates. However, the accuracy of the irradiance sensor has negligible impact on degradation rate uncertainty emphasizing that precision (relative accuracy) is more important than absolute accuracy.
Stochastic and epistemic uncertainty propagation in LCA
DEFF Research Database (Denmark)
Clavreul, Julie; Guyonnet, Dominique; Tonini, Davide
2013-01-01
When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However, the selection of single probability distributions appears often arbitrary when faced with scarce information...... or expert judgement (epistemic uncertainty). The possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent...... of epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted as Independent Random Set, IRS) generalizes the process of random sampling...
Uncertainty for Part Density Determination: An Update
Energy Technology Data Exchange (ETDEWEB)
Valdez, Mario Orlando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-12-14
Accurate and precise density measurements by hydrostatic weighing requires the use of an analytical balance, configured with a suspension system, to both measure the weight of a part in water and in air. Additionally, the densities of these liquid media (water and air) must be precisely known for the part density determination. To validate the accuracy and precision of these measurements, uncertainty statements are required. The work in this report is a revision of an original report written more than a decade ago, specifically applying principles and guidelines suggested by the Guide to the Expression of Uncertainty in Measurement (GUM) for determining the part density uncertainty through sensitivity analysis. In this work, updated derivations are provided; an original example is revised with the updated derivations and appendix, provided solely to uncertainty evaluations using Monte Carlo techniques, specifically using the NIST Uncertainty Machine, as a viable alternative method.
Uncertainty under quantum measures and quantum memory
Xiao, Yunlong; Jing, Naihuan; Li-Jost, Xianqing
2017-04-01
The uncertainty principle restricts potential information one gains about physical properties of the measured particle. However, if the particle is prepared in entanglement with a quantum memory, the corresponding entropic uncertainty relation will vary. Based on the knowledge of correlations between the measured particle and quantum memory, we have investigated the entropic uncertainty relations for two and multiple measurements and generalized the lower bounds on the sum of Shannon entropies without quantum side information to those that allow quantum memory. In particular, we have obtained generalization of Kaniewski-Tomamichel-Wehner's bound for effective measures and majorization bounds for noneffective measures to allow quantum side information. Furthermore, we have derived several strong bounds for the entropic uncertainty relations in the presence of quantum memory for two and multiple measurements. Finally, potential applications of our results to entanglement witnesses are discussed via the entropic uncertainty relation in the absence of quantum memory.
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
Uncertainty Quantification in Hybrid Dynamical Systems
Sahai, Tuhin
2011-01-01
Uncertainty quantification (UQ) techniques are frequently used to ascertain output variability in systems with parametric uncertainty. Traditional algorithms for UQ are either system-agnostic and slow (such as Monte Carlo) or fast with stringent assumptions on smoothness (such as polynomial chaos and Quasi-Monte Carlo). In this work, we develop a fast UQ approach for hybrid dynamical systems by extending the polynomial chaos methodology to these systems. To capture discontinuities, we use a wavelet-based Wiener-Haar expansion. We develop a boundary layer approach to propagate uncertainty through separable reset conditions. We also introduce a transport theory based approach for propagating uncertainty through hybrid dynamical systems. Here the expansion yields a set of hyperbolic equations that are solved by integrating along characteristics. The solution of the partial differential equation along the characteristics allows one to quantify uncertainty in hybrid or switching dynamical systems. The above method...
Uncertainty quantification in hybrid dynamical systems
Sahai, Tuhin; Pasini, José Miguel
2013-03-01
Uncertainty quantification (UQ) techniques are frequently used to ascertain output variability in systems with parametric uncertainty. Traditional algorithms for UQ are either system-agnostic and slow (such as Monte Carlo) or fast with stringent assumptions on smoothness (such as polynomial chaos and Quasi-Monte Carlo). In this work, we develop a fast UQ approach for hybrid dynamical systems by extending the polynomial chaos methodology to these systems. To capture discontinuities, we use a wavelet-based Wiener-Haar expansion. We develop a boundary layer approach to propagate uncertainty through separable reset conditions. We also introduce a transport theory based approach for propagating uncertainty through hybrid dynamical systems. Here the expansion yields a set of hyperbolic equations that are solved by integrating along characteristics. The solution of the partial differential equation along the characteristics allows one to quantify uncertainty in hybrid or switching dynamical systems. The above methods are demonstrated on example problems.
Modeling uncertainty in geographic information and analysis
Institute of Scientific and Technical Information of China (English)
2008-01-01
Uncertainty modeling and data quality for spatial data and spatial analyses are im-portant topics in geographic information science together with space and time in geography,as well as spatial analysis. In the past two decades,a lot of efforts have been made to research the uncertainty modeling for spatial data and analyses. This paper presents our work in the research. In particular,four progresses in the re-search are given out: (a) from determinedness-to uncertainty-based representation of geographic objects in GIS; (b) from uncertainty modeling for static data to dy-namic spatial analyses; (c) from modeling uncertainty for spatial data to models; and (d) from error descriptions to quality control for spatial data.
Dealing with uncertainties - communication between disciplines
Overbeek, Bernadet; Bessembinder, Janette
2013-04-01
Climate adaptation research inevitably involves uncertainty issues - whether people are building a model, using climate scenarios, or evaluating policy processes. However, do they know which uncertainties are relevant in their field of work? And which uncertainties exist in the data from other disciplines that they use (e.g. climate data, land use, hydrological data) and how they propagate? From experiences in Dutch research programmes on climate change in the Netherlands we know that disciplines often deal differently with uncertainties. This complicates communication between disciplines and also with the various users of data and information on climate change and its impacts. In October 2012 an autumn school was organized within the Knowledge for Climate Research Programme in the Netherlands with as central theme dealing with and communicating about uncertainties, in climate- and socio-economic scenarios, in impact models and in the decision making process. The lectures and discussions contributed to the development of a common frame of reference (CFR) for dealing with uncertainties. The common frame contains the following: 1. Common definitions (typology of uncertainties, robustness); 2. Common understanding (why do we consider it important to take uncertainties into account) and aspects on which we disagree (how far should scientists go in communication?); 3. Documents that are considered important by all participants; 4. Do's and don'ts in dealing with uncertainties and communicating about uncertainties (e.g. know your audience, check how your figures are interpreted); 5. Recommendations for further actions (e.g. need for a platform to exchange experiences). The CFR is meant to help researchers in climate adaptation to work together and communicate together on climate change (better interaction between disciplines). It is also meant to help researchers to explain to others (e.g. decision makers) why and when researchers agree and when and why they disagree
Treatment of Uncertainties in Probabilistic Tsunami Hazard
Thio, H. K.
2012-12-01
Over the last few years, we have developed a framework for developing probabilistic tsunami inundation maps, which includes comprehensive quantification of earthquake recurrence as well as uncertainties, and applied it to the development of a tsunami hazard map of California. The various uncertainties in tsunami source and propagation models are an integral part of a comprehensive probabilistic tsunami hazard analysis (PTHA), and often drive the hazard at low probability levels (i.e. long return periods). There is no unique manner in which uncertainties are included in the analysis although in general, we distinguish between "natural" or aleatory variability, such as slip distribution and event magnitude, and uncertainties due to an incomplete understanding of the behavior of the earth, called epistemic uncertainties, such as scaling relations and rupture segmentation. Aleatory uncertainties are typically included through integration over distribution functions based on regression analyses, whereas epistemic uncertainties are included using logic trees. We will discuss how the different uncertainties were included in our recent probabilistic tsunami inundation maps for California, and their relative importance on the final results. Including these uncertainties in offshore exceedance waveheights is straightforward, but the problem becomes more complicated once the non-linearity of near-shore propagation and inundation are encountered. By using the probabilistic off-shore waveheights as input level for the inundation models, the uncertainties up to that point can be included in the final maps. PTHA provides a consistent analysis of tsunami hazard and will become an important tool in diverse areas such as coastal engineering and land use planning. The inclusive nature of the analysis, where few assumptions are made a-priori as to which sources are significant, means that a single analysis can provide a comprehensive view of the hazard and its dominant sources
The legal status of Uncertainty
Altamura, M.; Ferraris, L.; Miozzo, D.; Musso, L.; Siccardi, F.
2011-03-01
An exponential improvement of numerical weather prediction (NWP) models was observed during the last decade (Lynch, 2008). Civil Protection (CP) systems exploited Meteo services in order to redeploy their actions towards the prediction and prevention of events rather than towards an exclusively response-oriented mechanism1. Nevertheless, experience tells us that NWP models, even if assisted by real time observations, are far from being deterministic. Complications frequently emerge in medium to long range forecasting, which are subject to sudden modifications. On the other hand, short term forecasts, if seen through the lens of criminal trials2, are to the same extent, scarcely reliable (Molini et al., 2009). One particular episode related with wrong forecasts, in the Italian panorama, has deeply frightened CP operators as the NWP model in force missed a meteorological adversity which, in fact, caused death and dealt severe damage in the province of Vibo Valentia (2006). This event turned into a very discussed trial, lasting over three years, and intended against whom assumed the legal position of guardianship within the CP. A first set of data is now available showing that in concomitance with the trial of Vibo Valentia the number of alerts issued raised almost three folds. We sustain the hypothesis that the beginning of the process of overcriminalization (Husak, 2008) of CPs is currently increasing the number of false alerts with the consequent effect of weakening alert perception and response by the citizenship (Brezntiz, 1984). The common misunderstanding of such an issue, i.e. the inherent uncertainty in weather predictions, mainly by prosecutors and judges, and generally by whom deals with law and justice, is creating the basis for a defensive behaviour3 within CPs. This paper intends, thus, to analyse the social and legal relevance of uncertainty in the process of issuing meteo-hydrological alerts by CPs. Footnotes: 1 The Italian Civil Protection is working
The legal status of Uncertainty
Directory of Open Access Journals (Sweden)
M. Altamura
2011-03-01
Full Text Available An exponential improvement of numerical weather prediction (NWP models was observed during the last decade (Lynch, 2008. Civil Protection (CP systems exploited Meteo services in order to redeploy their actions towards the prediction and prevention of events rather than towards an exclusively response-oriented mechanism^{1}.
Nevertheless, experience tells us that NWP models, even if assisted by real time observations, are far from being deterministic. Complications frequently emerge in medium to long range forecasting, which are subject to sudden modifications. On the other hand, short term forecasts, if seen through the lens of criminal trials^{2}, are to the same extent, scarcely reliable (Molini et al., 2009.
One particular episode related with wrong forecasts, in the Italian panorama, has deeply frightened CP operators as the NWP model in force missed a meteorological adversity which, in fact, caused death and dealt severe damage in the province of Vibo Valentia (2006. This event turned into a very discussed trial, lasting over three years, and intended against whom assumed the legal position of guardianship within the CP. A first set of data is now available showing that in concomitance with the trial of Vibo Valentia the number of alerts issued raised almost three folds. We sustain the hypothesis that the beginning of the process of overcriminalization (Husak, 2008 of CPs is currently increasing the number of false alerts with the consequent effect of weakening alert perception and response by the citizenship (Brezntiz, 1984.
The common misunderstanding of such an issue, i.e. the inherent uncertainty in weather predictions, mainly by prosecutors and judges, and generally by whom deals with law and justice, is creating the basis for a defensive behaviour^{3} within CPs. This paper intends, thus, to analyse the social and legal relevance of uncertainty in the process of issuing
Conference on information processing and management of uncertainty
Marsala, Christophe; Rifqi, Maria; Yager, Ronald R
2008-01-01
Intelligent systems are necessary to handle modern computer-based technologies managing information and knowledge. This book discusses the theories required to help provide solutions to difficult problems in the construction of intelligent systems. Particular attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of a linguistic nature. The main aspects of clustering, classification, summarization, decision making and systems modeling are also addressed. Topics covered in the book include fundamental issues in uncertainty, the rap
Markov decision processes in natural resources management: observability and uncertainty
Williams, Byron K.
2015-01-01
The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.
Measurement Uncertainty Analysis of the Strain Gauge Based Stabilographic Platform
Directory of Open Access Journals (Sweden)
Walendziuk Wojciech
2014-08-01
Full Text Available The present article describes constructing a stabilographic platform which records a standing patient’s deflection from their point of balance. The constructed device is composed of a toughen glass slab propped with 4 force sensors. Power transducers are connected to the measurement system based on a 24-bit ADC transducer which acquires slight body movements of a patient. The data is then transferred to the computer in real time and data analysis is conducted. The article explains the principle of operation as well as the algorithm of measurement uncertainty for the COP (Centre of Pressure surface (x, y.
The Perihelion Precession of Mercury and the Generalized Uncertainty Principle
Majumder, Barun
2011-01-01
Very recently authors in [1] proposed a new Generalized Uncertainty Principle (or GUP) with a linear term in Plank length. In this Letter the effect of this linear term is studied perturbatively in the context of Keplerian orbits. The angle by which the perihelion of the orbit revolves over a complete orbital cycle is computed. The result is applied in the context of the precession of the perihelion of Mercury. As a consequence we get a lower bound of the new intermediate length scale offered by the GUP which is approximately 40 orders of magnitude below Plank length.
Uncertainty and sensitivity analyses in seismic risk assessments on the example of Cologne, Germany
Directory of Open Access Journals (Sweden)
S. Tyagunov
2013-12-01
Full Text Available Both aleatory and epistemic uncertainties associated with different sources and components of risk (hazard, exposure, vulnerability are present at each step of seismic risk assessments. All individual sources of uncertainty contribute to the total uncertainty, which might be very high and, within the decision-making context, may therefore lead to either very conservative and expensive decisions or the perception of considerable risk. When anatomizing the structure of the total uncertainty, it is therefore important to propagate the different individual uncertainties through the computational chain and to quantify their contribution to the total value of risk. The present study analyzes different uncertainties associated with the hazard, vulnerability and loss components by the use of logic trees. The emphasis is on the analysis of epistemic uncertainties, which represent the reducible part of the total uncertainty, including a sensitivity analysis of the resulting seismic risk assessments with regards to the different uncertainty sources. This investigation, being a part of the EU FP7 project MATRIX (New Multi-Hazard and Multi-Risk Assessment Methods for Europe, is carried out for the example of, and with reference to, the conditions of the city of Cologne, Germany, which is one of the MATRIX test cases. At the same time, this particular study does not aim to revise nor to refine the hazard and risk level for Cologne; it is rather to show how large are the existing uncertainties and how they can influence seismic risk estimates, especially in less well-studied areas, if hazard and risk models adapted from other regions are used.
Uncertainty and sensitivity analyses in seismic risk assessments on the example of Cologne, Germany
Tyagunov, S.; Pittore, M.; Wieland, M.; Parolai, S.; Bindi, D.; Fleming, K.; Zschau, J.
2014-06-01
Both aleatory and epistemic uncertainties associated with different sources and components of risk (hazard, exposure, vulnerability) are present at each step of seismic risk assessments. All individual sources of uncertainty contribute to the total uncertainty, which might be very high and, within the decision-making context, may therefore lead to either very conservative and expensive decisions or the perception of considerable risk. When anatomizing the structure of the total uncertainty, it is therefore important to propagate the different individual uncertainties through the computational chain and to quantify their contribution to the total value of risk. The present study analyses different uncertainties associated with the hazard, vulnerability and loss components by the use of logic trees. The emphasis is on the analysis of epistemic uncertainties, which represent the reducible part of the total uncertainty, including a sensitivity analysis of the resulting seismic risk assessments with regard to the different uncertainty sources. This investigation, being a part of the EU FP7 project MATRIX (New Multi-Hazard and Multi-Risk Assessment Methods for Europe), is carried out for the example of, and with reference to, the conditions of the city of Cologne, Germany, which is one of the MATRIX test cases. At the same time, this particular study does not aim to revise nor to refine the hazard and risk level for Cologne; it is rather to show how large are the existing uncertainties and how they can influence seismic risk estimates, especially in less well-studied areas, if hazard and risk models adapted from other regions are used.
Uncertainty Quantification and Statistical Convergence Guidelines for PIV Data
Stegmeir, Matthew; Kassen, Dan
2016-11-01
As Particle Image Velocimetry has continued to mature, it has developed into a robust and flexible technique for velocimetry used by expert and non-expert users. While historical estimates of PIV accuracy have typically relied heavily on "rules of thumb" and analysis of idealized synthetic images, recently increased emphasis has been placed on better quantifying real-world PIV measurement uncertainty. Multiple techniques have been developed to provide per-vector instantaneous uncertainty estimates for PIV measurements. Often real-world experimental conditions introduce complications in collecting "optimal" data, and the effect of these conditions is important to consider when planning an experimental campaign. The current work utilizes the results of PIV Uncertainty Quantification techniques to develop a framework for PIV users to utilize estimated PIV confidence intervals to compute reliable data convergence criteria for optimal sampling of flow statistics. Results are compared using experimental and synthetic data, and recommended guidelines and procedures leveraging estimated PIV confidence intervals for efficient sampling for converged statistics are provided.
Ensemble Forecast: A New Approach to Uncertainty and Predictability
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic (or control) forecast after a short lead time (3 5 days) for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilistic quantitative precipitation forecast (PQPF). Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.
Uncertainty quantification for quantum chemical models of complex reaction networks.
Proppe, Jonny; Husch, Tamara; Simm, Gregor N; Reiher, Markus
2016-12-22
For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.
Efficient control variates for uncertainty quantification of radiation transport
Frankel, A.; Iaccarino, G.
2017-03-01
Numerical simulations of problems involving radiation transport are challenging because of the associated computational cost; moreover, it is typically difficult to describe the optical properties of the system very precisely, and therefore uncertainties abound. We aim to represent the uncertainties explicitly and to characterize their impact on the output of interest. While stochastic collocation and polynomial chaos methods have been applied previously, these methods can suffer from the curse of dimensionality and fail in cases where the system response is discontinuous or highly non-linear. Monte Carlo methods are more robust, but they converge slowly. To that end, we apply the control variate method to uncertainty propagation via Monte Carlo. We leverage the modeling hierarchy of radiation transport to use low fidelity models such as the diffusion approximation and coarse angular discretizations to reduce the confidence interval on the quantity of interest. The efficiency of the control variate method is demonstrated in several problems involving stochastic media, thermal emission, and radiation properties with different quantities of interest. The control variates are able to provide significant variance reduction and efficiency increase in all problems considered. We conclude our study with a discussion of choosing optimal control variates and other extensions of Monte Carlo methods.
Uncertainty Quantification for Polynomial Systems via Bernstein Expansions
Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.
2012-01-01
This paper presents a unifying framework to uncertainty quantification for systems having polynomial response metrics that depend on both aleatory and epistemic uncertainties. The approach proposed, which is based on the Bernstein expansions of polynomials, enables bounding the range of moments and failure probabilities of response metrics as well as finding supersets of the extreme epistemic realizations where the limits of such ranges occur. These bounds and supersets, whose analytical structure renders them free of approximation error, can be made arbitrarily tight with additional computational effort. Furthermore, this framework enables determining the importance of particular uncertain parameters according to the extent to which they affect the first two moments of response metrics and failure probabilities. This analysis enables determining the parameters that should be considered uncertain as well as those that can be assumed to be constants without incurring significant error. The analytical nature of the approach eliminates the numerical error that characterizes the sampling-based techniques commonly used to propagate aleatory uncertainties as well as the possibility of under predicting the range of the statistic of interest that may result from searching for the best- and worstcase epistemic values via nonlinear optimization or sampling.
A python framework for environmental model uncertainty analysis
White, Jeremy; Fienen, Michael; Doherty, John E.
2016-01-01
We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.
On Uncertainty Quantification of Lithium-ion Batteries
Hadigol, Mohammad; Doostan, Alireza
2015-01-01
In this work, a stochastic, physics-based model for Lithium-ion batteries (LIBs) is presented in order to study the effects of model uncertainties on the cell capacity, voltage, and concentrations. To this end, the proposed uncertainty quantification (UQ) approach, based on sparse polynomial chaos expansions, relies on a small number of battery simulations. Within this UQ framework, the identification of most important uncertainty sources is achieved by performing a global sensitivity analysis via computing the so-called Sobol' indices. Such information aids in designing more efficient and targeted quality control procedures, which consequently may result in reducing the LIB production cost. An LiC$_6$/LiCoO$_2$ cell with 19 uncertain parameters discharged at 0.25C, 1C and 4C rates is considered to study the performance and accuracy of the proposed UQ approach. The results suggest that, for the considered cell, the battery discharge rate is a key factor affecting not only the performance variability of the ce...
Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
Directory of Open Access Journals (Sweden)
Elise Payzan-LeNestour
Full Text Available Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.
Rahman, A.; Tsai, F.T.-C.; White, C.D.; Willson, C.S.
2008-01-01
This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis. ?? 2008 ASCE.
The topology of geology 2: Topological uncertainty
Thiele, Samuel T.; Jessell, Mark W.; Lindsay, Mark; Wellmann, J. Florian; Pakyuz-Charrier, Evren
2016-10-01
Uncertainty is ubiquitous in geology, and efforts to characterise and communicate it are becoming increasingly important. Recent studies have quantified differences between perturbed geological models to gain insight into uncertainty. We build on this approach by quantifying differences in topology, a property that describes geological relationships in a model, introducing the concept of topological uncertainty. Data defining implicit geological models were perturbed to simulate data uncertainties, and the amount of topological variation in the resulting model suite measured to provide probabilistic assessments of specific topological hypotheses, sources of topological uncertainty and the classification of possible model realisations based on their topology. Overall, topology was found to be highly sensitive to small variations in model construction parameters in realistic models, with almost all of the several thousand realisations defining distinct topologies. In particular, uncertainty related to faults and unconformities was found to have profound topological implications. Finally, possible uses of topology as a geodiversity metric and validation filter are discussed, and methods of incorporating topological uncertainty into physical models are suggested.
Uncertainty of measurement: an immunology laboratory perspective.
Beck, Sarah C; Lock, Robert J
2015-01-01
'Measurement uncertainty of measured quantity values' (ISO15189) requires that the laboratory shall determine the measurement uncertainty for procedures used to report measured quantity values on patients' samples. Where we have numeric data measurement uncertainty can be expressed as the standard deviation or as the co-efficient of variation. However, in immunology many of the assays are reported either as semi-quantitative (i.e. an antibody titre) or qualitative (positive or negative) results. In the latter context, measuring uncertainty is considerably more difficult. There are, however, strategies which can allow us to minimise uncertainty. A number of parameters can contribute to making measurements uncertain. These include bias, precision, standard uncertainty (expressed as standard deviation or coefficient of variation), sensitivity, specificity, repeatability, reproducibility and verification. Closely linked to these are traceability and standardisation. In this article we explore the challenges presented to immunology with regard to measurement uncertainty. Many of these challenges apply equally to other disciplines working with qualitative or semi-quantitative data.
Constructing the uncertainty of due dates.
Vos, Sarah C; Anthony, Kathryn E; O'Hair, H Dan
2014-01-01
By its nature, the date that a baby is predicted to be born, or the due date, is uncertain. How women construct the uncertainty of their due dates may have implications for when and how women give birth. In the United States as many as 15% of births occur before 39 weeks because of elective inductions or cesarean sections, putting these babies at risk for increased medical problems after birth and later in life. This qualitative study employs a grounded theory approach to understand the decisions women make on how and when to give birth. Thirty-three women who were pregnant or had given birth within the past 2 years participated in key informant or small-group interviews. The results suggest that women interpret the uncertainty of their due dates as a reason to wait for birth and as a reason to start the process early; however, information about a baby's brain development in the final weeks of pregnancy may persuade women to remain pregnant longer. The uncertainties of due dates are analyzed using Babrow's problematic integration, which distinguishes between epistemological and ontological uncertainty. The results point to a third type of uncertainty, axiological uncertainty. Axiological uncertainty is rooted in the values and ethics of outcomes.
Uncertainty quantification approaches for advanced reactor analyses.
Energy Technology Data Exchange (ETDEWEB)
Briggs, L. L.; Nuclear Engineering Division
2009-03-24
The original approach to nuclear reactor design or safety analyses was to make very conservative modeling assumptions so as to ensure meeting the required safety margins. Traditional regulation, as established by the U. S. Nuclear Regulatory Commission required conservatisms which have subsequently been shown to be excessive. The commission has therefore moved away from excessively conservative evaluations and has determined best-estimate calculations to be an acceptable alternative to conservative models, provided the best-estimate results are accompanied by an uncertainty evaluation which can demonstrate that, when a set of analysis cases which statistically account for uncertainties of all types are generated, there is a 95% probability that at least 95% of the cases meet the safety margins. To date, nearly all published work addressing uncertainty evaluations of nuclear power plant calculations has focused on light water reactors and on large-break loss-of-coolant accident (LBLOCA) analyses. However, there is nothing in the uncertainty evaluation methodologies that is limited to a specific type of reactor or to specific types of plant scenarios. These same methodologies can be equally well applied to analyses for high-temperature gas-cooled reactors and to liquid metal reactors, and they can be applied to steady-state calculations, operational transients, or severe accident scenarios. This report reviews and compares both statistical and deterministic uncertainty evaluation approaches. Recommendations are given for selection of an uncertainty methodology and for considerations to be factored into the process of evaluating uncertainties for advanced reactor best-estimate analyses.
An approach of sensitivity and uncertainty analyses methods installation in a safety calculation
Energy Technology Data Exchange (ETDEWEB)
Pepin, G.; Sallaberry, C. [Agence nationale pour la gestion des dechets radioactifs (Andra), DS/CS, 92 - Chatenay-Malabry (France)
2003-07-01
Simulation of the migration in deep geological formations leads to solve convection-diffusion equations in porous media, associated with the computation of hydrogeologic flow. Different time-scales (simulation during 1 million years), scales of space, contrasts of properties in the calculation domain, are taken into account. This document deals more particularly with uncertainties on the input data of the model. These uncertainties are taken into account in total analysis with the use of uncertainty and sensitivity analysis. ANDRA (French national agency for the management of radioactive wastes) carries out studies on the treatment of input data uncertainties and their propagation in the models of safety, in order to be able to quantify the influence of input data uncertainties of the models on the various indicators of safety selected. The step taken by ANDRA consists initially of 2 studies undertaken in parallel: - the first consists of an international review of the choices retained by ANDRA foreign counterparts to carry out their uncertainty and sensitivity analysis, - the second relates to a review of the various methods being able to be used in sensitivity and uncertainty analysis in the context of ANDRA's safety calculations. Then, these studies are supplemented by a comparison of the principal methods on a test case which gathers all the specific constraints (physical, numerical and data-processing) of the problem studied by ANDRA.
Energy Technology Data Exchange (ETDEWEB)
J.C. Helton
2000-04-19
This presentation investigates the incorporation of uncertainty and variability of drip shield and waste package degradation in analyses with the Waste Package Degradation (WAPDEG) program (CRWMS M&O 1998). This plan was developed in accordance with Development Plan TDP-EBS-MD-000020 (CRWMS M&O 1999a). Topics considered include (1) the nature of uncertainty and variability (Section 6.1), (2) incorporation of variability and uncertainty into analyses involving individual patches, waste packages, groups of waste packages, and the entire repository (Section 6.2), (3) computational strategies (Section 6.3), (4) incorporation of multiple waste package layers (i.e., drip shield, Alloy 22, and stainless steel) into an analysis (Section 6.4), (5) uncertainty in the characterization of variability (Section 6.5), and (6) Gaussian variance partitioning (Section 6.6). The presentation ends with a brief concluding discussion (Section 7).
Mean-value second-order uncertainty analysis method: application to water quality modelling
Mailhot, Alain; Villeneuve, Jean-Pierre
Uncertainty analysis in hydrology and water quality modelling is an important issue. Various methods have been proposed to estimate uncertainties on model results based on given uncertainties on model parameters. Among these methods, the mean-value first-order second-moment (MFOSM) method and the advanced mean-value first-order second-moment (AFOSM) method are the most common ones. This paper presents a method based on a second-order approximation of a model output function. The application of this method requires the estimation of first- and second-order derivatives at a mean-value point in the parameter space. Application to a Streeter-Phelps prototype model is presented. Uncertainties on two and six parameters are considered. Exceedance probabilities (EP) of dissolved oxygen concentrations are obtained and compared with EP computed using Monte Carlo, AFOSM and MFOSM methods. These results show that the mean-value second-order method leads to better estimates of EP.
Energy Technology Data Exchange (ETDEWEB)
Park, S. H.; Park, S. Y.; Kim, K. R.; Ahn, K. I. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2009-10-15
Uncertainty analysis is an essential element of safety analysis of nuclear power plants, and especially on the increase as an essential methodology of safety assessment by computer codes. Recently, these efforts have been stepped up to apply the uncertainty methodology in severe accident analysis and PSA Level 2. From this point of view, a statistical sampling-based MAAP-specific platform for a severe accident uncertainty analysis, SAUNA, is being developed in KAERI. Its main purpose is to execute many simulations that are employed for uncertainty analysis. For its efficient implementation, the SAUNA system is composed of three related modules: Firstly, a module for preparing a statistical sampling matrix, secondly, a module for the dynamic linking between code and samples for code simulation, and thirdly, a postprocessing module for further analysis of the code simulation results. The main objective of this paper is to introduce the main functions of the SAUNA system and its example of implementation.
Uncertainty quantification for proton-proton fusion in chiral effective field theory
Acharya, B; Ekström, A; Forssén, C; Platter, L
2016-01-01
We compute the $S$-factor of the proton-proton ($pp$) fusion reaction using chiral effective field theory ($\\chi$EFT) up to next-to-next-to-leading order (NNLO) and perform a rigorous uncertainty analysis of the results. We quantify the uncertainties due to (i) the computational method used to compute the $pp$ cross section in momentum space, (ii) the statistical uncertainties in the low-energy coupling constants of $\\chi$EFT, (iii) the systematic uncertainty due to the $\\chi$EFT cutoff, and (iv) systematic variations in the database used to calibrate the nucleon-nucleon interaction. We also examine the robustness of the polynomial extrapolation procedure, which is commonly used to extract the threshold $S$-factor and its energy-derivatives. By performing a statistical analysis of the polynomial fit of the energy-dependent $S$-factor at several different energy intervals, we eliminate a systematic uncertainty that can arise from the choice of the fit interval in our calculations. In addition, we explore the s...
Directory of Open Access Journals (Sweden)
Liang Xue
2017-02-01
Full Text Available Reservoir simulations always involve a large number of parameters to characterize the properties of formation and fluid, many of which are subject to uncertainties owing to spatial heterogeneity and insufficient measurements. To provide solutions to uncertainty-related issues in reservoir simulations, a general package called GenPack has been developed. GenPack includes three main functions required for full stochastic analysis in petroleum engineering, generation of random parameter fields, predictive uncertainty quantifications and automatic history matching. GenPack, which was developed in a modularized manner, is a non-intrusive package which can be integrated with any existing commercial simulator in petroleum engineering to facilitate its application. Computational efficiency can be improved both theoretically by introducing a surrogate model-based probabilistic collocation method, and technically by using parallel computing. A series of synthetic cases are designed to demonstrate the capability of GenPack. The test results show that the random parameter field can be flexibly generated in a customized manner for petroleum engineering applications. The predictive uncertainty can be reasonably quantified and the computational efficiency is significantly improved. The ensemble Kalman filter (EnKF-based automatic history matching method can improve predictive accuracy and reduce the corresponding predictive uncertainty by accounting for observations.
Where does quantum uncertainty come from?
DEFF Research Database (Denmark)
Rozpędek, Filip; Kaniewski, Jedrzej; Coles, Patrick J.
2016-01-01
about the exact state of the physical system. Here, we critically examine the concept of preparation uncertainty and ask whether similarly in the quantum regime, some of the uncertainty that we observe can actually also be understood as a lack of information, albeit a lack of quantum information. We...... to show that also for other measurements the amount of uncertainty is in part connected to a lack of information. Finally, we discuss the conceptual implications of our observation to the security of cryptographic protocols that make use of BB84 states....
Uncertainty: the Curate's egg in financial economics.
Pixley, Jocelyn
2014-06-01
Economic theories of uncertainty are unpopular with financial experts. As sociologists, we rightly refuse predictions, but the uncertainties of money are constantly sifted and turned into semi-denial by a financial economics set on somehow beating the future. Picking out 'bits' of the future as 'risk' and 'parts' as 'information' is attractive but socially dangerous, I argue, because money's promises are always uncertain. New studies of uncertainty are reversing sociology's neglect of the unavoidable inability to know the forces that will shape the financial future.
On the Uncertainty in the Intercultural Communication
Institute of Scientific and Technical Information of China (English)
Pan Dong
2008-01-01
Uncertainty is associated negatively with positive expectations,communication satisfaction and quality of communication.And it is one of the key factors that influence our communication with others.So how to reduce uncertainty to a moderate and ideal state in intercultural communication is very important,which decides whether the communication is successful.In this paper,the author tentatively suggests several means to reduce uncertainty,such as shared knowledge,linguistic knowledge,stereotype and personal knowledge,relaxed and equal atmosphere,openness and tolerance and so on with a hope to fan flames in the successful intercultural communication.
The uncertainty budget in pharmaceutical industry
DEFF Research Database (Denmark)
Heydorn, Kaj
Measurements in a pharmaceutical industry are usually carried out to ascertain the quality of a product or the control of a process; in either case the measurement result serves to demonstrate that the value of the measurand is within specified limits. No method is without bias, and no result...... of their uncertainty, exactly as described in GUM [2]. Pharmaceutical industry has therefore over the last 5 years shown increasing interest in accreditation according to ISO 17025 [3], and today uncertainty budgets are being developed for all so-called critical measurements. The uncertainty of results obtained...
Sources of Uncertainty in Rainfall Maps from Cellular Communication Networks
Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko
2015-04-01
Accurate measurements of rainfall are important in many hydrological applications, for instance, flash-flood early-warning systems, hydraulic structures design, agriculture, weather forecasting, and climate modelling. Rainfall intensities can be retrieved from (commercial) microwave link networks. Whenever possible, link networks measure and store the decrease in power of the electromagnetic signal at regular intervals. The decrease in power is largely due to the attenuation by raindrops along the link paths. Such an alternative technique fulfills the continuous strive for measurements of rainfall in time and space at higher resolutions, especially in places where traditional rain gauge networks are scarce or poorly maintained. Rainfall maps from microwave link networks have recently been introduced at country-wide scales. Despite their potential in rainfall estimation at high spatiotemporal resolutions, the uncertainties present in rainfall maps from link networks are not yet fully comprehended. The aim of this work is to identify and quantify the sources of uncertainty present in interpolated rainfall maps from link rainfall depths. In order to disentangle these sources of uncertainty, we classified them into two categories: (1) those associated with the individual microwave link measurements, i.e., the physics involved in the measurements such as wet antenna attenuation, sampling interval of measurements, wet/dry period classification, drop size distribution (DSD), and multi-path propagation; (2) those associated with mapping, i.e., the combined effect of the interpolation methodology, the spatial density of the network, and the availability of link measurements. We computed ~ 3500 rainfall maps from real and simulated link rainfall depths for 12 days for the land surface of The Netherlands. These rainfall maps were compared against quality-controlled gauge-adjusted radar rainfall fields (assumed to be the ground truth). Thus, we were able to not only identify
Uncertainty in perception and the Hierarchical Gaussian Filter
Directory of Open Access Journals (Sweden)
Christoph Daniel Mathys
2014-11-01
Full Text Available In its full sense, perception rests on an agent’s model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the hierarchical Gaussian filter (HGF offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (instability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF’s hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient - but at the same time intuitive - framework for the resolution of perceptual uncertainty in behaving agents.
Precipitation interpolation and corresponding uncertainty assessment using copulas
Bardossy, A.; Pegram, G. G.
2012-12-01
Spatial interpolation of rainfall over different time and spatial scales is necessary in many applications of hydrometeorology. The specific problems encountered in rainfall interpolation include: the large number of calculations which need to be performed automatically the quantification of the influence of topography, usually the most influential of exogenous variables how to use observed zero (dry) values in interpolation, because their proportion increases the shorter the time interval the need to estimate a reasonable uncertainty of the modelled point/pixel distributions the need to separate (i) temporally highly correlated bias from (ii) random interpolation errors at different spatial and temporal scales the difficulty of estimating uncertainty of accumulations over a range of spatial scales. The approaches used and described in the presentation employ the variables rainfall and altitude. The methods of interpolation include (i) Ordinary Kriging of the rainfall without altitude, (ii) External Drift Kriging with altitude as an exogenous variable, and less conventionally, (iii) truncated Gaussian copulas and truncated v-copulas, both omitting and including the altitude of the control stations as well as that of the target (iv) truncated Gaussian copulas and truncated v-copulas for a two-step interpolation of precipitation combining temporal and spatial quantiles for bias quantification. It was found that truncated Gaussian copulas, with the target's and all control the stations' altitudes included as exogenous variables, produce the lowest Mean Square error in cross-validation and, as a bonus, model with the least bias. In contrast, the uncertainty of interpolation is better described by the v-copulas, but the Gaussian copulas have the advantage of computational effort (by three orders of magnitude) which justifies their use in practice. It turns out that the uncertainty estimates of the OK and EDK interpolants are not competitive at any time scale, from daily
A FUZZY UNCERTAINTY COMPENSATOR FOR MANIPULATOR TRAJECTORY TRACKING
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A novel fuzzy logic compensating (FLC) scheme is proposed to enhance the conventional computed-torque control (CTC) structure of manipulators. The control scheme is based on the combination of a classical CTC and FLC, and the resulting control scheme has a simple structure with improved robustness. Further improvement of the performance of the FLC scheme is achieved through automatic tuning of a weight parameter ( leading to a self-tuning fuzzy logic compensator, so the system uncertainty can be compensated very well. By taking into account the full nonlinear nature of the robotic dynamics, the overall closed-loop system is shown to be asymptotically stable. Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensation scheme to control a manipulator during a trajectory tracking task.