Готові списки джерел за темами / Uncertainly quantification

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Добірка наукової літератури з теми "Uncertainly quantification"

Автор: Grafiati
Опубліковано: 14 грудня 2024

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Статті в журналах з теми "Uncertainly quantification"

1

Jalaian, Brian, Michael Lee, and Stephen Russell. "Uncertain Context: Uncertainty Quantification in Machine Learning." AI Magazine 40, no. 4 (December 20, 2019): 40–49. http://dx.doi.org/10.1609/aimag.v40i4.4812.

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.
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2

Verdonck, H., O. Hach, J. D. Polman, O. Braun, C. Balzani, S. Müller, and J. Rieke. "-An open-source framework for the uncertainty quantification of aeroelastic wind turbine simulation tools." Journal of Physics: Conference Series 2265, no. 4 (May 1, 2022): 042039. http://dx.doi.org/10.1088/1742-6596/2265/4/042039.

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Abstract The uncertainty quantification of aeroelastic wind turbine simulations is an active research topic. This paper presents a dedicated, open-source framework for this purpose. The framework is built around the uncertainpy package, likewise available as open source. Uncertainty quantification is done with a non-intrusive, global and variance-based surrogate model, using PCE (i.e., polynomial chaos expansion). Two methods to handle the uncertain parameter distribution along the blades are presented. The framework is demonstrated on the basis of an aeroelastic stability analysis. A sensitivity analysis is performed on the influence of the flapwise, edgewise and torsional stiffness of the blades on the damping of the most critical mode for both a Bladed linearization and a Bladed time domain simulation. The sensitivities of both models are in excellent agreement and the PCE surrogate models are shown to be accurate approximations of the true models.
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3

Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (September 1, 2022): 6379. http://dx.doi.org/10.3390/en15176379.

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Анотація:
The Special Issue “Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems” comprises nine articles that present important applications of concepts for performing sensitivity analyses and uncertainty quantifications of models of nuclear energy systems [...]
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4

Oh, Deog Yeon, Young Seok Bang, Kwang Won Seul, and Sweng Woong Woo. "ICONE23-1466 UNCERTAINTY QUANTIFICATION OF PHYSICAL MODELS USING CIRCE METHOD." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_213.

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5

Hu, Juxi, Lei Wang, and Xiaojun Wang. "Non-Probabilistic Uncertainty Quantification of Fiber-Reinforced Composite Laminate Based on Micro- and Macro-Mechanical Analysis." Applied Sciences 12, no. 22 (November 18, 2022): 11739. http://dx.doi.org/10.3390/app122211739.

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In this paper, the main aim is to study and predict macro elastic mechanical parameters of fiber-reinforced composite laminates by combining micro-mechanical analysis models and the non-probabilistic set theory. It deals with uncertain input parameters existing in quantification models as interval variables. Here, several kinds of micro-mechanical mathematical models are introduced, and the parameter vertex solution theorem and the Monte Carlo simulation method can be used to perform uncertainty quantification of macro elastic properties for composites. In order to take the correlations between macro elastic properties into consideration, the obtained limited sample points or experimental data are utilized on the basis of the grey mathematical modeling theory, where correlated uncertain macro parameters can be treated as a closed and bounded convex polyhedral model. It can give out a clear analytical description for feasible domains of correlated macro elastic properties in the process of uncertainty quantification. Finally, two numerical examples are carried out to account for the validity and feasibility of the proposed quantification method. The results show that the proposed method can become a powerful and meaningful supplement for uncertainty quantification of composite laminates and provide data support for further uncertainty propagation analysis.
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6

Sun, X., T. Kirchdoerfer, and M. Ortiz. "Rigorous uncertainty quantification and design with uncertain material models." International Journal of Impact Engineering 136 (February 2020): 103418. http://dx.doi.org/10.1016/j.ijimpeng.2019.103418.

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7

Cheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person, and Joe Wiart. "A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data." International Journal of Environmental Research and Public Health 17, no. 7 (April 9, 2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.

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This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.
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8

Ernst, Oliver, Fabio Nobile, Claudia Schillings, and Tim Sullivan. "Uncertainty Quantification." Oberwolfach Reports 16, no. 1 (February 26, 2020): 695–772. http://dx.doi.org/10.4171/owr/2019/12.

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9

Salehghaffari, S., and M. Rais-Rohani. "Material model uncertainty quantification using evidence theory." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 10 (January 8, 2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.

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Uncertainties in material models and their influence on structural behavior and reliability are important considerations in analysis and design of structures. In this article, a methodology based on the evidence theory is presented for uncertainty quantification of constitutive models. The proposed methodology is applied to Johnson–Cook plasticity model while considering various sources of uncertainty emanating from experimental stress–strain data as well as method of fitting the model constants and representation of the nondimensional temperature. All uncertain parameters are represented in interval form. Rules for agreement, conflict, and ignorance relationships in the data are discussed and subsequently used to construct a belief structure for each uncertain material parameter. The material model uncertainties are propagated through nonlinear crush simulation of an aluminium alloy 6061-T6 circular tube under axial impact load. Surrogate modeling and global optimization techniques are used for efficient calculation of the propagated belief structure of the tube response, whereas Yager’s aggregation rule of evidence is used for multi-model consideration. Evidence-based uncertainty in the structural response is measured and presented in terms of belief, plausibility, and plausibility-decision values.
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10

Tuczyński, Tomasz, and Jerzy Stopa. "Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm." Energies 16, no. 3 (January 20, 2023): 1153. http://dx.doi.org/10.3390/en16031153.

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Анотація:
Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.
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Дисертації з теми "Uncertainly quantification"

1

Nguyen, Trieu Nhat Thanh. "Modélisation et simulation d'éléments finis du système pelvien humain vers un outil d'aide à la décision fiable : incertitude des données et des lois de comportement." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0015.

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Анотація:
Cette thèse a développé une approche originale pour quantifier les incertitudes liées aux propriétés hyperélastiques des tissus mous, en utilisant à la fois des probabilités précises et imprécises. Le protocole de calcul a été étendu pour quantifier les incertitudes dans les contractions utérines actives lors des simulations du deuxième stade du travail. De plus, une simulation de la descente foetale a été créée, intégrant des données de contraction utérine active basées sur l'IRM et une quantification d'incertitude associée. L'étude a révélé que l'Expansion du Chaos Polynomial (PCE) non intrusif est plus efficace que les simulations directes de Monte Carlo.Ce travail met en évidence l'importance de quantifier et de propager les incertitudes dans les propriétés hyperélastiques des tissus utérins lors des simulations de travail, améliorant ainsi la fiabilité des résultats de simulation. Pour la première fois, il aborde la quantification des incertitudes des contractions utérines actives pendant le travail, assurant des résultats de simulation fiables et valides. La simulation de la descente foetale, cohérente avec les données théoriques et IRM, valide la précision des modèles en reflétant les processus physiologiques, soulignant la nécessité d'inclure les contractions utérines actives pour des résultats plus réalistes. L'étude souligne également l'importance d'évaluer la sensibilité globale des paramètres, l'incertitude et les résultats de simulation pour des applications cliniques fiables. En conclusion, cette recherche fait progresser de manière significative les simulations de l'accouchement en fournissant un cadre robuste pour la quantification des incertitudes, améliorant ainsi la fiabilité des résultats de simulation et soutenant une meilleure prise de décision clinique.Les travaux futurs étendront le processus à un modèle complet du système pelvien, incluant l'os du bassin, les ligaments et d'autres organes (comme la vessie, le rectum) pour simuler l'ensemble du processus de délivrance. Des comportements plus complexes des tissus mous pelviens seront étudiés pour mieux décrire l'interaction foetale pendant le travail. L'utilisation de données IRM 3D, si disponibles, permettra une meilleure évaluation, notamment pour la rotation foetale lors de l'expulsion. Un modèle complet du bassin maternel sera couplé à l'apprentissage par renforcement pour identifier les mécanismes de délivrance. De plus, une combinaison plus complexe d'orientations de fibres sera envisagée. Pour améliorer la méthode de Monte Carlo, des techniques de réduction de la variance et des stratégies d'optimisation telles que l'échantillonnage par importance, l'échantillonnage hypercube latin et les méthodes de Monte Carlo par chaînes de Markov seront utilisées pour réduire la taille des échantillons tout en maintenant la précision. Des méthodes pour une convergence plus rapide et une précision accrue dans la quantification des incertitudes, comme discuté par Hauseux et al. (2017), seront explorées. D'autres formulations de la méthode des éléments finis stochastiques (SFEM), comme la méthode SFEM spectrale stochastique (SSFEM), seront considérées pour la quantification des incertitudes, et des méthodes intrusives comme le stochastique-Galerkin seront utilisées pour leurs avantages computationnels. Ces approches pourraient améliorer la quantification des incertitudes dans les études futures.Enfin, l'approche développée pourrait être adaptée à la modélisation spécifique au patient et aux simulations de complications de la délivrance, permettant d'identifier les risques et les solutions thérapeutiques potentielles pour des interventions médicales personnalisées et des résultats améliorés pour les patients
Approximately 0.5 million deaths during childbirth occur annually, as reported by the World Health Organization (WHO). One prominent cause is complicated obstructed labor, also known as labor dystocia. This condition arises when the baby fails to navigate the birth canal despite normal uterine contractions. Therefore, understanding this complex physiological process is essential for improving diagnosis, optimizing clinical interventions, and defining predictive and preventive strategies. Currently, due to the complexity of experimental protocols and associated ethical issues, computational modeling and simulation of childbirth have emerged as the most promising solutions to achieve these objectives. However, it is crucial to quantify the significant influence of inherent uncertainties in the parameters and behaviors of the human pelvic system and their propagation through simulations to establish reliable indicators for clinical decision-making. Specifically, epistemic uncertainties due to lack of knowledge and aleatoric uncertainties due to intrinsic variability in physical domain geometries, material properties, and loads are often not fully understood and are frequently overlooked in current literature on childbirth computational modeling and simulation.This PhD thesis addresses three original contributions aimed at overcoming these challenges: 1) development and evaluation of a computational workflow for the uncertainty quantification of hyperelastic properties of the soft tissue using precise and imprecise probabilities; 2) extrapolation of the developed protocol for the uncertainty quantification of the active uterine contraction during the second stage of labor simulation; and 3) development and evaluation of a fetus descent simulation with the active uterine contraction using MRI-based observations and associated uncertainty quantification process.This thesis pays the way to a more reliable childbirth modeling and simulation under passive and active uterine contractions. In fact, the developed computational protocols could be extrapolated into a patient-specific modeling and simulation to identify the risk factors and associated strategies for vaginal delivery complications in a straightforward manner. Finally, the investigation of stochastic finite element formulation will allow to improve the computational cost for the uncertainty quantification process
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2

Elfverson, Daniel. "Multiscale Methods and Uncertainty Quantification." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262354.

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In this thesis we consider two great challenges in computer simulations of partial differential equations: multiscale data, varying over multiple scales in space and time, and data uncertainty, due to lack of or inexact measurements. We develop a multiscale method based on a coarse scale correction, using localized fine scale computations. We prove that the error in the solution produced by the multiscale method decays independently of the fine scale variation in the data or the computational domain. We consider the following aspects of multiscale methods: continuous and discontinuous underlying numerical methods, adaptivity, convection-diffusion problems, Petrov-Galerkin formulation, and complex geometries. For uncertainty quantification problems we consider the estimation of p-quantiles and failure probability. We use spatial a posteriori error estimates to develop and improve variance reduction techniques for Monte Carlo methods. We improve standard Monte Carlo methods for computing p-quantiles and multilevel Monte Carlo methods for computing failure probability.
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3

Parkinson, Matthew. "Uncertainty quantification in Radiative Transport." Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767610.

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We study how uncertainty in the input data of the Radiative Transport equation (RTE), affects the distribution of (functionals of) its solution (the output data). The RTE is an integro-differential equation, in up to seven independent variables, that models the behaviour of rarefied particles (such as photons and neutrons) in a domain. Its applications include nuclear reactor design, radiation shielding, medical imaging, optical tomography and astrophysics. We focus on the RTE in the context of nuclear reactor physics where, to design and maintain safe reactors, understanding the effects of uncertainty is of great importance. There are many potential sources of uncertainty within a nuclear reactor. These include the geometry of the reactor, the material composition and reactor wear. Here we consider uncertainty in the macroscopic cross-sections ('the coefficients'), representing them as correlated spatial random fields. We wish to estimate the statistics of a problem-specific quantity of interest (under the influence of the given uncertainty in the cross-sections), which is defined as a functional of the scalar flux. This is the forward problem of Uncertainty Quantification. We seek accurate and efficient methods for estimating these statistics. Thus far, the research community studying Uncertainty Quantification in radiative transport has focused on the Polynomial Chaos expansion. However, it is known that the number of terms in the expansion grows exponentially with respect to the number of stochastic dimensions and the order of the expansion, i.e. polynomial chaos suffers from the curse of dimensionality. Instead, we focus our attention on variants of Monte Carlo sampling - studying standard and quasi-Monte Carlo methods, and their multilevel and multi-index variants. We show numerically that the quasi-Monte Carlo rules, and the multilevel variance reduction techniques, give substantial gains over the standard Monte Carlo method for a variety of radiative transport problems. Moreover, we report problems in up to 3600 stochastic dimensions, far beyond the capability of polynomial chaos. A large part of this thesis is focused towards a rigorous proof that the multilevel Monte Carlo method is superior to the standard Monte Carlo method, for the RTE in one spatial and one angular dimension with random cross-sections. This is the first rigorous theory of Uncertainty Quantification for transport problems and the first rigorous theory for Uncertainty Quantification for any PDE problem which accounts for a path-dependent stability condition. To achieve this result, we first present an error analysis (including a stability bound on the discretisation parameters) for the combined spatial and angular discretisation of the spatially heterogeneous RTE, which is explicit in the heterogeneous coefficients. We can then extend this result to prove probabilistic bounds on the error, under assumptions on the statistics of the cross-sections and provided the discretisation satisfies the stability condition pathwise. The multilevel Monte Carlo complexity result follows. Amongst other novel contributions, we: introduce a method which combines a direct and iterative solver to accelerate the computation of the scalar flux, by adaptively choosing the fastest solver based on the given coefficients; numerically test an iterative eigensolver, which uses a single source iteration within each loop of a shifted inverse power iteration; and propose a novel model for (random) heterogeneity in concrete which generates (piecewise) discontinuous coefficients according to the material type, but where the composition of materials are spatially correlated.
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4

Carson, J. "Uncertainty quantification in palaeoclimate reconstruction." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29076/.

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Анотація:
Studying the dynamics of the palaeoclimate is a challenging problem. Part of the challenge lies in the fact that our understanding must be based on only a single realisation of the climate system. With only one climate history, it is essential that palaeoclimate data are used to their full extent, and that uncertainties arising from both data and modelling are well characterised. This is the motivation behind this thesis, which explores approaches for uncertainty quantification in problems related to palaeoclimate reconstruction. We focus on uncertainty quantification problems for the glacial-interglacial cycle, namely parameter estimation, model comparison, and age estimation of palaeoclimate observations. We develop principled data assimilation schemes that allow us to assimilate palaeoclimate data into phenomenological models of the glacial-interglacial cycle. The statistical and modelling approaches we take in this thesis means that this amounts to the task of performing Bayesian inference for multivariate stochastic differential equations that are only partially observed. One contribution of this thesis is the synthesis of recent methodological advances in approximate Bayesian computation and particle filter methods. We provide an up-to-date overview that relates the different approaches and provides new insights into their performance. Through simulation studies we compare these approaches using a common benchmark, and in doing so we highlight the relative strengths and weaknesses of each method. There are two main scientific contributions in this thesis. The first is that by using inference methods to jointly perform parameter estimation and model comparison, we demonstrate that the current two-stage practice of first estimating observation times, and then treating them as fixed for subsequent analysis, leads to conclusions that are not robust to the methods used for estimating the observation times. The second main contribution is the development of a novel age model based on a linear sediment accumulation model. By extending the target of the particle filter we are able to jointly perform parameter estimation, model comparison, and observation age estimation. In doing so, we are able to perform palaeoclimate reconstruction using sediment core data that takes age uncertainty in the data into account, thus solving the problem of dating uncertainty highlighted above.
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5

Boopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.

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6

Kalmikov, Alexander G. "Uncertainty Quantification in ocean state estimation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79291.

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Анотація:
Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 158-160).
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circulation and Climate of the Oceans" (ECCO) consortium has developed a scalable system for dynamically consistent estimation of global time-evolving ocean state by optimal combination of ocean general circulation model (GCM) with diverse ocean observations. The estimation system is based on the "adjoint method" solution of an unconstrained least-squares optimization problem formulated with the method of Lagrange multipliers for fitting the dynamical ocean model to observations. The dynamical consistency requirement of ocean state estimation necessitates this approach over sequential data assimilation and reanalysis smoothing techniques. In addition, it is computationally advantageous because calculation and storage of large covariance matrices is not required. However, this is also a drawback of the adjoint method, which lacks a native formalism for error propagation and quantification of assimilated uncertainty. The objective of this dissertation is to resolve that limitation by developing a feasible computational methodology for uncertainty analysis in dynamically consistent state estimation, applicable to the large dimensionality of global ocean models. Hessian (second derivative-based) methodology is developed for Uncertainty Quantification (UQ) in large-scale ocean state estimation, extending the gradient-based adjoint method to employ the second order geometry information of the model-data misfit function in a high-dimensional control space. Large error covariance matrices are evaluated by inverting the Hessian matrix with the developed scalable matrix-free numerical linear algebra algorithms. Hessian-vector product and Jacobian derivative codes of the MIT general circulation model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A Lanczos algorithm is applied for extracting the leading rank eigenvectors and eigenvalues of the Hessian matrix. The eigenvectors represent the constrained uncertainty patterns. The inverse eigenvalues are the corresponding uncertainties. The dimensionality of UQ calculations is reduced by eliminating the uncertainty null-space unconstrained by the supplied observations. Inverse and forward uncertainty propagation schemes are designed for assimilating observation and control variable uncertainties, and for projecting these uncertainties onto oceanographic target quantities. Two versions of these schemes are developed: one evaluates reduction of prior uncertainties, while another does not require prior assumptions. The analysis of uncertainty propagation in the ocean model is time-resolving. It captures the dynamics of uncertainty evolution and reveals transient and stationary uncertainty regimes. The system is applied to quantifying uncertainties of Antarctic Circumpolar Current (ACC) transport in a global barotropic configuration of the MITgcm. The model is constrained by synthetic observations of sea surface height and velocities. The control space consists of two-dimensional maps of initial and boundary conditions and model parameters. The size of the Hessian matrix is 0(1010) elements, which would require 0(60GB) of uncompressed storage. It is demonstrated how the choice of observations and their geographic coverage determines the reduction in uncertainties of the estimated transport. The system also yields information on how well the control fields are constrained by the observations. The effects of controls uncertainty reduction due to decrease of diagonal covariance terms are compared to dynamical coupling of controls through off-diagonal covariance terms. The correlations of controls introduced by observation uncertainty assimilation are found to dominate the reduction of uncertainty of transport. An idealized analytical model of ACC guides a detailed time-resolving understanding of uncertainty dynamics. Keywords: Adjoint model uncertainty, sensitivity, posterior error reduction, reduced rank Hessian matrix, Automatic Differentiation, ocean state estimation, barotropic model, Drake Passage transport.
by Alexander G. Kalmikov.
Ph.D.
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7

Malenova, Gabriela. "Uncertainty quantification for high frequency waves." Licentiate thesis, KTH, Numerisk analys, NA, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186287.

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Анотація:
We consider high frequency waves satisfying the scalar wave equationwith highly oscillatory initial data. The speed of propagation of the mediumas well as the phase and amplitude of the initial data is assumed to beuncertain, described by a finite number of independent random variables withknown probability distributions. We introduce quantities of interest (QoIs)aslocal averages of the squared modulus of the wave solution, or itsderivatives.The regularity of these QoIs in terms of the input random parameters and thewavelength is important for uncertainty quantification methods based oninterpolation in the stochastic space. In particular, the size of thederivativesshould be bounded and independent of the wavelength. In the contributedpapers, we show that the QoIs indeed have this property, despite the highlyoscillatory character of the waves.

QC 20160510

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8

Roy, Pamphile. "Uncertainty quantification in high dimensional problems." Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0038.

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Анотація:
Les incertitudes font partie du monde qui nous entoure. Se limiter à une seule valeur nominale est bien souvent trop restrictif, et ce d'autant plus lorsqu'il est question de systèmes complexes. Comprendre la nature et l'impact de ces incertitudes est devenu un aspect important de tout travail d'ingénierie. D'un point de vue sociétal, les incertitudes jouent un rôle important dans les processus de décision. Les dernières recommandations de la Commission européenne en matière d'analyses des risques souligne l'importance du traitement des incertitudes. Afin de comprendre les incertitudes, une nouvelle discipline mathématique appelée la quantification des incertitudes a été créée. Ce domaine regroupe un large éventail de méthodes d'analyse statistique qui visent à lier des perturbations sur les paramètres d'entrée d'un système (plan d'expérience) à une quantité d'intérêt. L'objectif de ce travail de thèse est de proposer des améliorations sur divers aspects méthodologiques de la quantification des incertitudes dans le cadre de simulation numérique coûteuse. Cela passe par une utilisation des méthodes existantes avec une approche multi-stratégie mais aussi la création de nouvelles méthodes. Dans ce contexte, de nouvelles méthodes d'échantillonnage et de ré-échantillonnage ont été développées afin de mieux capturer la variabilité dans le cas d'un problème de grande dimension. Par ailleurs, de nouvelles méthodes de visualisation des incertitudes sont proposées dans le cas d'une grande dimension des paramètres d'entrée et d'une grande dimension de la quantité d'intérêt. Les méthodes développées peuvent être utilisées dans divers domaines comme la modélisation hydraulique ou encore la modélisation aérodynamique. Leur apport est démontré sur des systèmes réalistes en faisant appel à des outils de mécanique des fluides numérique. Enfin, ces méthodes ne sont pas seulement utilisables dans le cadre de simulation numérique, mais elles peuvent être utilisées sur de réels dispositifs expérimentaux
Uncertainties are predominant in the world that we know. Referring therefore to a nominal value is too restrictive, especially when it comes to complex systems. Understanding the nature and the impact of these uncertainties has become an important aspect of engineering work. On a societal point of view, uncertainties play a role in terms of decision-making. From the European Commission through the Better Regulation Guideline, impact assessments are now advised to take uncertainties into account. In order to understand the uncertainties, the mathematical field of uncertainty quantification has been formed. UQ encompasses a large palette of statistical tools and it seeks to link a set of input perturbations on a system (design of experiments) towards a quantity of interest. The purpose of this work is to propose improvements on various methodological aspects of uncertainty quantification applied to costly numerical simulations. This is achieved by using existing methods with a multi-strategy approach but also by creating new methods. In this context, novel sampling and resampling approaches have been developed to better capture the variability of the physical phenomenon when dealing with a high number of perturbed inputs. These allow to reduce the number of simulation required to describe the system. Moreover, novel methods are proposed to visualize uncertainties when dealing with either a high dimensional input parameter space or a high dimensional quantity of interest. The developed methods can be used in various fields like hydraulic modelling and aerodynamic modelling. Their capabilities are demonstrated in realistic systems using well established computational fluid dynamics tools. Lastly, they are not limited to the use of numerical experiments and can be used equally for real experiments
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Alvarado, Martin Guillermo. "Quantification of uncertainty during history matching." Texas A&M University, 2003. http://hdl.handle.net/1969/463.

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Jimenez, Edwin. "Uncertainty quantification of nonlinear stochastic phenomena." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-161351/.

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Анотація:
Thesis (Ph. D.)--Florida State University, 2009.
Advisor: M.Y. Hussaini, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed on Mar. 16, 2010). Document formatted into pages; contains xii, 113 pages. Includes bibliographical references.
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Книги з теми "Uncertainly quantification"

1

Soize, Christian. Uncertainty Quantification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0.

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Sullivan, T. J. Introduction to Uncertainty Quantification. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23395-6.

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Ghanem, Roger, David Higdon, and Houman Owhadi, eds. Handbook of Uncertainty Quantification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6.

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Souza de Cursi, Eduardo. Uncertainty Quantification using R. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17785-9.

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Souza de Cursi, Eduardo. Uncertainty Quantification with R. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48208-3.

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Le Maître, O. P., and Omar M. Knio. Spectral Methods for Uncertainty Quantification. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2.

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Dienstfrey, Andrew M., and Ronald F. Boisvert, eds. Uncertainty Quantification in Scientific Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6.

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McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.

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Bijl, Hester, Didier Lucor, Siddhartha Mishra, and Christoph Schwab, eds. Uncertainty Quantification in Computational Fluid Dynamics. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00885-1.

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Bardsley, Johnathan M. Computational Uncertainty Quantification for Inverse Problems. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975383.

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Частини книг з теми "Uncertainly quantification"

1

Soize, Christian. "Fundamental Notions in Stochastic Modeling of Uncertainties and Their Propagation in Computational Models." In Uncertainty Quantification, 1–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_1.

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Soize, Christian. "Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media." In Uncertainty Quantification, 245–300. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_10.

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3

Soize, Christian. "Elements of Probability Theory." In Uncertainty Quantification, 17–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_2.

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4

Soize, Christian. "Markov Process and Stochastic Differential Equation." In Uncertainty Quantification, 41–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_3.

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Soize, Christian. "MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors." In Uncertainty Quantification, 61–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_4.

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Soize, Christian. "Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties." In Uncertainty Quantification, 77–132. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_5.

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Soize, Christian. "Brief Overview of Stochastic Solvers for the Propagation of Uncertainties." In Uncertainty Quantification, 133–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_6.

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8

Soize, Christian. "Fundamental Tools for Statistical Inverse Problems." In Uncertainty Quantification, 141–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_7.

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9

Soize, Christian. "Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics." In Uncertainty Quantification, 155–216. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_8.

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Soize, Christian. "Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design." In Uncertainty Quantification, 217–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_9.

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Тези доповідей конференцій з теми "Uncertainly quantification"

1

Misaka, Takashi, Shigeru Obayashi, and Shinkyu Jeong. "Uncertainly Quantification of Lidar-Derived Wake Vortex Parameters with/without Data Assimilation (Invited)." In 8th AIAA Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3271.

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Zhang, Qian, Shenren Xu, Xianjun Yu, Jiaxin Liu, Dingxi Wang, and Xiuquan Huang. "Quantification of Compressor Aerodynamic Performance Deviation due to Manufacturing Uncertainty Using the Adjoint Method." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-59.

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Efficient and accurate Uncertainly Quantification (UQ) is essential for robust design optimization and manufacturing tolerance control to limit performance deterioration and scattering of compressor blades. Built upon past work, the current study investigates the applicability of the newly proposed adjoint method based nonlinear model for accurate and efficient Uncertainly Quantification (UQ) of performance deviations of a transonic compressor blade due to geometric variability. To demonstrate the advantages of the adjoint method based nonlinear model, two other methods, namely, the adjoint method based linear model and the high-fidelity computational fluid dynamics method are used to produce reference results. To keep the computational cost tractable for the high-fidelity method, a section of a transonic compressor blade is taken as the test case. In order to draw a more general conclusion, UQ analyses are performed for three different performance metrics (mass flow rate, efficiency and pressure ratio) at two typical operating conditions (the design condition and a near-stall condition). The accuracy of UQ results by the adjoint method based nonlinear model and its cost time are compared with those of the other two methods.
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3

Lee, Nian-Ze, Yen-Shi Wang, and Jie-Hong R. Jiang. "Solving Stochastic Boolean Satisfiability under Random-Exist Quantification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/96.

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Stochastic Boolean Satisfiability (SSAT) is a powerful formalism to represent computational problems with uncertainly, such as belief network inference and propositional probabilistic planning. Solving SSAT formulas lies in the same complexity class (PSPACE-complete) as solving Quantified Boolean Formula (QBF). While many endeavors have been made to enhance QBF solving, SSAT has drawn relatively less attention in recent years. This paper focuses on random-exist quantified SSAT formulas, and proposes an algorithm combining binary decision diagram (BDD), logic synthesis, and modern SAT techniques to improve computational efficiency. Unlike prior exact SSAT algorithms, the proposed method can be easily modified to solve approximate SSAT by deriving upper and lower bounds of satisfying probability. Experimental results show that our method outperforms the state-of-the-art algorithm on random k-CNF formulas and has effective application to approximate SSAT on circuit benchmarks.
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4

Kotteda, V. M. Krushnarao, Anitha Kommu, Vinod Kumar, and William Spotz. "Uncertainty Quantification of a Fluidized Bed Reactor." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4844.

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Abstract Fluidized beds are used in a wide range of applications in gasification, combustion, and process engineering. Multiphase flow in such applications involves numerous uncertain parameters. Uncertainty quantification provides uncertainty in syngas yield and efficiency of coal/biomass gasification in a power plant. Techniques such as sensitivity analysis are useful in identifying parameters that have the most influence on the quantities of interest. Also, it helps to decrease the computational cost of the uncertainty quantification and optimize the reactor. We carried out a nondeterministic analysis of flow in a biomass reactor. The flow in the reactor is simulated with National Energy Technology Laboratory’s open source multiphase fluid dynamics suite MFiX. It does not possess tools for uncertainty quantification. Therefore, we developed a C++ wrapper to integrate an uncertainty quantification toolkit developed at Sandia National Laboratory with MFiX. The wrapper exchanges uncertain input parameters and critical output parameters among Dakota and MFiX. We quantify uncertainty in key output parameters via a sampling method. In addition, sensitivity analysis is carried out for all eight uncertain input parameters namely particle-particle restitution coefficient, angle of internal friction, coefficient of friction between two-phases, velocity of the fluidizing agent at the inlet, velocity of the biomass particles at the inlet, diameter of the biomass particles, viscosity of the fluidizing agent, and the percentage of nitrogen/oxygen in the fluidizing agent.
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Jayaraman, Buvana, Manas Khurana, Andrew Wissink, and Rohit Jain. "Uncertainty Quantification Approach for Rotorcraft Simulations." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17462.

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The goal of this work is to quantify the uncertainty and sensitivity of freestream velocity and wind direction on wing download and rotor thrust predictions for the Joint Vertical Experiment (JVX) tiltrotor configuration in hover. Even light winds can have a significant impact on hover performance. Accordingly, an accurate representation of hover performance with uncertainties due to variability in atmospheric wind conditions needs to be understood. To support this effort, mid-fidelity simulations with a Reduced Order Aerodynamic Model in CREATETM-AV Helios is used to generate training and testing data for constructing the surrogate models. Uncertainty propagation is facilitated using a surrogate-based approach which integrates stochastic expansions based non-intrusive polynomial chaos method in the Dakota�environment. The first test case considers wind velocity and directions treated as epistemic uncertain variables. Post uncertainty analysis, parameter sensitivities are established using Sobol indices to rank the relative contribution of input parameters to the total uncertainty in download and thrust. Sensitivity analysis showed that the interaction of wind velocity and direction has the largest influence on download predictions. The second case includes collective as an uncertain input and additionally carries out a sensitivity analysis. Computed Sobol indices identified collective as the major contributor of uncertainty. Ultimately, uncertainty quantification procedure laid out in this work can facilitate informed design decisions based on quantifiable data that is formed using validated computational approaches integrated with established data science principles with statistical metrics.
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Budzien, Joanne, James Byerly, Rob Aulwes, Rao Garimella, Angela Herring, and Jon Woodring. "Linking Material Models Between Codes: Establishing Thermodynamic Consistency." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86808.

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Abstract One increasingly important workflow for multiphysics simulations is linking simulation codes that have different physics models and different regimes for which they have been optimized. The science question for this scoping work was evaluating the compatibility of physics models on both sides of a link to ensure a smooth simulation continuation was possible. The VVUQ aspects were establishing the most important physics aspects for a credible simulation. The most important aspect was determined to be thermodynamic consistency such that nothing unphysical would be encountered during the simulation. The second most important aspect is ensuring adequate handling of mechanical deformation. The specific problem was driving a Taylor cylinder into an infinitely hard wall. The material was cerium, which has a complicated enough phase diagram to show some interesting thermodynamic behavior during the deformation. The main software involved is Abaqus for the initial simulation, Zelda (a LANL code) for linking, and FLAG (a LANL Lagrangian finite volume code). The basic process is using nominally the same material models in both Abaqus and FLAG to: • perform a calculation in Abaqus • output an ODB file from Abaqus with fields (e.g., density, stress) • use Zelda to extract fields and remap them onto a new mesh suitable for FLAG • continue the simulation in FLAG The remapping of fields onto the new mesh is a negligible source of error. Thermodynamic consistency is a much larger source of overall error and can be large enough to prevent initialization in the receiving code. The situation arises because of the way that the two codes treat different fields. Both codes have interpolation processes for evaluating the thermodynamics. Differences in which variable is primary and which is interpolated lead to numerical errors that can be irrelevant in one code and unusably large in the other code. This paper will explain the VVUQ issues in linking the codes, even with nominally the same material models, and propose some activities to answer some important VVUQ questions.
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Eça, L., K. Dowding, and P. J. Roache. "On the Application of the Area Metric to Validation Exercises of Stochastic Simulations." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86809.

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Abstract This paper discusses the application of the Area Metric to the quantification of modeling errors. The focus of the discussion is the effect of the shape of the two distributions on the result produced by the Area Metric. Two different examples that assume negligible experimental and numerical errors are presented: the first case has experimental and simulated quantities of interest defined by normal distributions that require the definition of a mean value and a standard deviation; the second example is taken from the V&V10.1 ASME Standard that applies the Area Metric to quantify the modeling error of the tip deflection of a loaded hollow tapered cantilever beam simulated with the static Bernoulli-Euler beam theory. The first example, shows that relatively small differences between the mean values are sufficient for the area metric to be insensitive to the standard deviation. Furthermore, the example of the V&V10.1 ASME Standard produces an Area Metric equal to the difference between the mean values of experiments and simulations. Therefore, the error quantification is reduced to a single number that is obtained from a simple difference of two mean values. This means that the Area Metric fails to reflect a dependence for the difference in the shape of the distributions representing variability. The paper also presents an alternative version of the Area Metric that does not filter the effect of the shape of the distributions by utilizing a reference simulation that has the same mean value of the experiments. This means that the quantification of the modeling error will have contributions from the difference in mean values and from the shape of the distributions.
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Davis, Brad, Gregory Langone, and Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.

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Abstract Periodic updates to small caliber weapon systems and projectiles used in military and law enforcement have resulted in consistently increasing material penetration capabilities. With each new generation, ballistics technology outpaces the lifecycle replacement of live-fire training facilities. For this reason, it is necessary to develop and maintain constitutive material models for use in analyzing the effects new threats will have on existing facilities and for designing new training facilities using numerical methods. This project utilizes material testing data to characterize cellular concretes used in the construction of live-fire training facilities with a 13-parameter Holmquist-Johnson-Cook (HJC) concrete constitutive model. Various statistical tools are used in this analysis to successfully describe the importance of each model parameter and quantify their uncertainty. First, Bayesian linear regression was used to calibrate the parameters in the strength and pressure components of the HJC material model given testing data of cellular concrete. These uncertain parameters were then used to construct computer simulations of penetration and perforation experiments that were previously conducted by Collard and Lanham. Then, Latin Hypercube Sampling of the parameter space was used to generate training data for a Gaussian Process surrogate model of the computer simulation. Using the surrogate model, a global variance-based sensitivity analysis of the material model was completed by computing main and total effect Sobol indices. Finally, a Bayesian calibration of the computer simulation based on the physical experiments was conducted to fully characterize the stochastic behavior of the material subjected to perforation impacts. These approaches can be used to inform decision makers about the potential risk associated with existing facilities and by designers of future live fire training facilities.
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"VVUQ2022 Front Matter." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-fm1.

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Kirsch, Jared, Nima Fathi, and Joshua Hubbard. "Validation Analysis of Medium-Scale Methanol Pool Fire." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86806.

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Abstract A 30-cm diameter methanol pool fire was modeled using Sandia National Laboratories SIERRA/Fuego turbulent reacting flow code. Large Eddy Simulation (LES) with subgrid turbulent kinetic energy closure was used as the turbulence model. Combustion was modeled and simulated using a strained laminar flamelet library approach. Radiative heat transfer was modeled using the gray-gas approximation. In this investigation, the area validation metric (AVM) is employed to examine simulation results against experimental data. Time-averaged values of temperature and axial velocity at multiple locations along the domain centerline are analyzed for two computational meshes. Two time ranges for averaging temperature and axial velocity are evaluated, and the relationship between the results and the underlying physics is mentioned. Flame height is estimated using an intermittency definition, and the effect of the threshold variable is discussed. Temperature and mixture fraction were used as threshold variables, and the sensitivity of flame height to changes in each is examined. This study aims to increase understanding of the simulation results in light of a specific validation metric, and serve as a start to further validation studies.
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Звіти організацій з теми "Uncertainly quantification"

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Caldeira, Joao. Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1623354.

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Urban, Nathan Mark. Climate Uncertainty Quantification at LANL. Office of Scientific and Technical Information (OSTI), April 2016. http://dx.doi.org/10.2172/1250690.

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Thiagarajan, J. Uncertainty Quantification in Scientific ML. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670557.

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Stracuzzi, David, Maximillian Chen, Michael Darling, Matthew Peterson, and Charlie Vollmer. Uncertainty Quantification for Machine Learning. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1733262.

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Karpius, Peter. Nuclide Identification, Quantification, and Uncertainty. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1782632.

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Croft, Stephen, and Andrew Nicholson. OR14-V-Uncertainty-PD2La Uncertainty Quantification Workshop Report. Office of Scientific and Technical Information (OSTI), July 2017. http://dx.doi.org/10.2172/1784220.

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Seifried, Jeffrey E. Adjoint-Based Uncertainty Quantification with MCNP. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1110395.

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Srinivasan, Gowri. Need for Uncertainty Quantification in Predictions. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1191117.

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De Bord, Sarah. Tutorial examples for uncertainty quantification methods. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1213490.

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Williams, Mark L. Whitepaper on Uncertainty Quantification for MPACT. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1255677.

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