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Thematische Bibliographien / Inverse Uncertainty Quantification / Dissertationen

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Dissertationen zum Thema „Inverse Uncertainty Quantification“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Hebbur, Venkata Subba Rao Vishwas. "Adjoint based solution and uncertainty quantification techniques for variational inverse problems." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76665.

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Variational inverse problems integrate computational simulations of physical phenomena with physical measurements in an informational feedback control system. Control parameters of the computational model are optimized such that the simulation results fit the physical measurements.The solution procedure is computationally expensive since it involves running the simulation computer model (the emph{forward model}) and the associated emph {adjoint model} multiple times. In practice, our knowledge of the underlying physics is incomplete and hence the associated computer model is laden with emph {

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Devathi, Duttaabhinivesh. "Uncertainty Quantification for Underdetermined Inverse Problems via Krylov Subspace Iterative Solvers." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case155446130705089.

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3

Chue, Bryan C. "Efficient Hessian computation in inverse problems with application to uncertainty quantification." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21138.

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Thesis (M.Sc.Eng.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>This thesis considers the efficient Hessian computation in inverse problems with specific application to the elastography inverse problem. Inverse problems use measurements of observable parameters to infer information about m

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4

Andersson, Hjalmar. "Inverse Uncertainty Quantification using deterministic sampling : An intercomparison between different IUQ methods." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447070.

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In this thesis, two novel methods for Inverse Uncertainty Quantification are benchmarked against the more established methods of Monte Carlo sampling of output parameters(MC) and Maximum Likelihood Estimation (MLE). Inverse Uncertainty Quantification (IUQ) is the process of how to best estimate the values of the input parameters in a simulation, and the uncertainty of said estimation, given a measurement of the output parameters. The two new methods are Deterministic Sampling (DS) and Weight Fixing (WF). Deterministic sampling uses a set of sampled points such that the set of points has the sa

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5

Lal, Rajnesh. "Data assimilation and uncertainty quantification in cardiovascular biomechanics." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS088/document.

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Les simulations numériques des écoulements sanguins cardiovasculaires peuvent combler d’importantes lacunes dans les capacités actuelles de traitement clinique. En effet, elles offrent des moyens non invasifs pour quantifier l’hémodynamique dans le cœur et les principaux vaisseaux sanguins chez les patients atteints de maladies cardiovasculaires. Ainsi, elles permettent de recouvrer les caractéristiques des écoulements sanguins qui ne peuvent pas être obtenues directement à partir de l’imagerie médicale. Dans ce sens, des simulations personnalisées utilisant des informations propres aux patien

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6

Narayanamurthi, Mahesh. "Advanced Time Integration Methods with Applications to Simulation, Inverse Problems, and Uncertainty Quantification." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104357.

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Simulation and optimization of complex physical systems are an integral part of modern science and engineering. The systems of interest in many fields have a multiphysics nature, with complex interactions between physical, chemical and in some cases even biological processes. This dissertation seeks to advance forward and adjoint numerical time integration methodologies for the simulation and optimization of semi-discretized multiphysics partial differential equations (PDEs), and to estimate and control numerical errors via a goal-oriented a posteriori error framework. We extend exponential

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7

Ray, Kolyan Michael. "Asymptotic theory for Bayesian nonparametric procedures in inverse problems." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/278387.

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The main goal of this thesis is to investigate the frequentist asymptotic properties of nonparametric Bayesian procedures in inverse problems and the Gaussian white noise model. In the first part, we study the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. This rate provides a quantitative measure of the quality of statistical estimation of the procedure. A theorem is proved in a general Hilbert space setting under approximation-theoretic assumptions on the prior. The result is applied to n

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8

Alhossen, Iman. "Méthode d'analyse de sensibilité et propagation inverse d'incertitude appliquées sur les modèles mathématiques dans les applications d'ingénierie." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30314/document.

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Dans de nombreuses disciplines, les approches permettant d'étudier et de quantifier l'influence de données incertaines sont devenues une nécessité. Bien que la propagation directe d'incertitudes ait été largement étudiée, la propagation inverse d'incertitudes demeure un vaste sujet d'étude, sans méthode standardisée. Dans cette thèse, une nouvelle méthode de propagation inverse d'incertitude est présentée. Le but de cette méthode est de déterminer l'incertitude d'entrée à partir de données de sortie considérées comme incertaines. Parallèlement, les méthodes d'analyse de sensibilité sont égalem

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Gehre, Matthias [Verfasser], Peter [Akademischer Betreuer] Maaß, and Bangti [Akademischer Betreuer] Jin. "Rapid Uncertainty Quantification for Nonlinear Inverse Problems / Matthias Gehre. Gutachter: Peter Maaß ; Bangti Jin. Betreuer: Peter Maaß." Bremen : Staats- und Universitätsbibliothek Bremen, 2013. http://d-nb.info/1072078589/34.

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10

Kamilis, Dimitrios. "Uncertainty Quantification for low-frequency Maxwell equations with stochastic conductivity models." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31415.

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Uncertainty Quantification (UQ) has been an active area of research in recent years with a wide range of applications in data and imaging sciences. In many problems, the source of uncertainty stems from an unknown parameter in the model. In physical and engineering systems for example, the parameters of the partial differential equation (PDE) that model the observed data may be unknown or incompletely specified. In such cases, one may use a probabilistic description based on prior information and formulate a forward UQ problem of characterising the uncertainty in the PDE solution and observati

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11

Attia, Ahmed Mohamed Mohamed. "Advanced Sampling Methods for Solving Large-Scale Inverse Problems." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73683.

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Ensemble and variational techniques have gained wide popularity as the two main approaches for solving data assimilation and inverse problems. The majority of the methods in these two approaches are derived (at least implicitly) under the assumption that the underlying probability distributions are Gaussian. It is well accepted, however, that the Gaussianity assumption is too restrictive when applied to large nonlinear models, nonlinear observation operators, and large levels of uncertainty. This work develops a family of fully non-Gaussian data assimilation algorithms that work by directly sa

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Galbally, David. "Nonlinear model reduction for uncertainty quantification in large-scale inverse problems : application to nonlinear convection-diffusion-reaction equation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43079.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.<br>Includes bibliographical references (p. 147-152).<br>There are multiple instances in science and engineering where quantities of interest are evaluated by solving one or several nonlinear partial differential equations (PDEs) that are parametrized in terms of a set of inputs. Even though well-established numerical techniques exist for solving these problems, their computational cost often precludes their use in cases where the outputs of interest must be evaluated repeatedly for different valu

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John, David Nicholas [Verfasser], and Vincent [Akademischer Betreuer] Heuveline. "Uncertainty quantification for an electric motor inverse problem - tackling the model discrepancy challenge / David Nicholas John ; Betreuer: Vincent Heuveline." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/122909265X/34.

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14

Cho, Taewon. "Computational Advancements for Solving Large-scale Inverse Problems." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103772.

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For many scientific applications, inverse problems have played a key role in solving important problems by enabling researchers to estimate desired parameters of a system from observed measurements. For example, large-scale inverse problems arise in many global problems and medical imaging problems such as greenhouse gas tracking and computational tomography reconstruction. This dissertation describes advancements in computational tools for solving large-scale inverse problems and for uncertainty quantification. Oftentimes, inverse problems are ill-posed and large-scale. Iterative projection m

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15

Boquet, Pujadas Aleix. "Variational approaches in inverse problems for image-based characterisation of cellular dynamics." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS558.pdf.

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Nous proposons une méthode pour calculer des grandeurs physiques telles que les gradients de pressions, les forces et les vitesses (p-f-u) nécessaires à la description des dynamiques cellulaires interne et externe et pour étudier les mécanismes biologiques qui les gouvernent. Cette méthode non invasive extrait le mouvement de l'objet biologique d'étude de son observation en microscopie de fluorescence conventionnelle, tout en inférant les variables d'un modèle physique décrivant son comportement. Cette idée est formulée comme un problème d'optimisation avec des dérivées partielles comme contra

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Perrin, Guillaume. "Random fields and associated statistical inverse problems for uncertainty quantification : application to railway track geometries for high-speed trains dynamical responses and risk assessment." Phd thesis, Université Paris-Est, 2013. http://pastel.archives-ouvertes.fr/pastel-01001045.

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Les nouvelles attentes vis-à-vis des nouveaux trains à grande vitesse sont nombreuses: on les voudrait plus rapides, plus confortables, plus stables, tout en étant moins consommateur d'énergie, moins agressif vis-à-vis des voies, moins bruyants... Afin d'optimiser la conception de ces trains du futur, il est alors nécessaire de pouvoir se baser sur une connaissance précise de l'ensemble des conditions de circulations qu'ils sont susceptibles de rencontrer au cours de leur cycle de vie. Afin de relever ces défis, la simulation a un très grand rôle à jouer. Pour que la simulation puisse être uti

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17

Carozzi, M. "AMMONIA EMISSIONS FROM ARABLE LANDS IN PO VALLEY: METHODOLOGIES, DYNAMICS AND QUANTIFICATION." Doctoral thesis, Università degli Studi di Milano, 2012. http://hdl.handle.net/2434/170268.

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Although the Po Valley (north Italy) is considered one of the most important ammonia (NH3) emitting regions in Europe, few data are available for an evaluation of the ammonia budget at field level in arable lands. Here the NH3 losses were quantify, considering different measurement and estimation approach, fertilisers and agronomic managements. The outputs of two concentration based-inverse dispersion models, together a mechanistic model were assessed with the direct measurements of ammonia fluxes by the micrometeorological technique eddy covariance, at hourly, daily and seasonal scales. A dis

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18

Cortesi, Andrea Francesco. "Predictive numerical simulations for rebuilding freestream conditions in atmospheric entry flows." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0021/document.

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Une prédiction fidèle des écoulements hypersoniques à haute enthalpie est capitale pour les missions d'entrée atmosphérique. Cependant, la présence d'incertitudes est inévitable, sur les conditions de l'écoulement libre comme sur d'autres paramètres des modèles physico-chimiques. Pour cette raison, une quantification rigoureuse de l'effet de ces incertitudes est obligatoire pour évaluer la robustesse et la prédictivité des simulations numériques. De plus, une reconstruction correcte des paramètres incertains à partir des mesures en vol peut aider à réduire le niveau d'incertitude sur les sorti

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19

Calatayud, Gregori Julia. "Computational methods for random differential equations: probability density function and estimation of the parameters." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/138396.

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[EN] Mathematical models based on deterministic differential equations do not take into account the inherent uncertainty of the physical phenomenon (in a wide sense) under study. In addition, inaccuracies in the collected data often arise due to errors in the measurements. It thus becomes necessary to treat the input parameters of the model as random quantities, in the form of random variables or stochastic processes. This gives rise to the study of random ordinary and partial differential equations. The computation of the probability density function of the stochastic solution is important

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20

Peyraut, Alice. "Modeling and Estimation of Pulmonary Poromechanics : towards a Robust High-Fidelity Digital Twin Approach for Idiopathic Pulmonary Fibrosis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX136.

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La Fibrose Pulmonaire Idiopathique (FPI) est une maladie au pronostic extrêmement sévère, qui affecte directement le parenchyme pulmonaire, et dont les mécanismes d’apparition et d’évolution restent encore mal compris. L’objectif de ce travail de thèse est d’approfondir la compréhension de la FPI en couplant modélisation biomécanique et traitement d’images biomédicales.Tout d'abord, une revue de la littérature relative à la FPI ainsi qu’aux modèles pulmonaires actuels a été effectuée. Un accent particulier est mis sur l’analyse des mécanismes qui pourraient expliquer l’évolution de cette patho

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21

Cioaca, Alexandru George. "A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/51795.

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A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimilation is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data ass

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22

Mondal, Anirban. "Bayesian Uncertainty Quantification for Large Scale Spatial Inverse Problems." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9905.

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We considered a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a high dimension spatial field. The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provides a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. Karhunen-Lo'eve expansion and Discrete Cosine transform were used for dimension reduction of the random spatial field.

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23

Flath, Hannah Pearl. "Hessian-based response surface approximations for uncertainty quantification in large-scale statistical inverse problems, with applications to groundwater flow." 2013. http://hdl.handle.net/2152/21157.

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Subsurface flow phenomena characterize many important societal issues in energy and the environment. A key feature of these problems is that subsurface properties are uncertain, due to the sparsity of direct observations of the subsurface. The Bayesian formulation of this inverse problem provides a systematic framework for inferring uncertainty in the properties given uncertainties in the data, the forward model, and prior knowledge of the properties. We address the problem: given noisy measurements of the head, the pdf describing the noise, prior information in the form of a pdf of the hydrau

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Sawlan, Zaid A. "Statistical Analysis and Bayesian Methods for Fatigue Life Prediction and Inverse Problems in Linear Time Dependent PDEs with Uncertainties." Diss., 2018. http://hdl.handle.net/10754/629731.

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This work employs statistical and Bayesian techniques to analyze mathematical forward models with several sources of uncertainty. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. One of the critical challenges in real-world applications is to quantify uncertainties of the unknown parameters using observations. To this purpose, methods based on the likelihood function, and Bayesian techniques constitute the two main statisti

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Martin, James Robert Ph D. "A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion." Thesis, 2015. http://hdl.handle.net/2152/31374.

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Quantifying uncertainties in large-scale forward and inverse PDE simulations has emerged as a central challenge facing the field of computational science and engineering. The promise of modeling and simulation for prediction, design, and control cannot be fully realized unless uncertainties in models are rigorously quantified, since this uncertainty can potentially overwhelm the computed result. While statistical inverse problems can be solved today for smaller models with a handful of uncertain parameters, this task is computationally intractable using contemporary algorithms for complex syst

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Yousefpour, Negin. "Comparative Deterministic and Probabilistic Modeling in Geotechnics: Applications to Stabilization of Organic Soils, Determination of Unknown Foundations for Bridge Scour, and One-Dimensional Diffusion Processes." Thesis, 2013. http://hdl.handle.net/1969.1/151268.

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This study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quan

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(11166777), Peiyi Zhang. "Langevinized Ensemble Kalman Filter for Large-Scale Dynamic Systems." Thesis, 2021.

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<p>The Ensemble Kalman filter (EnKF) has achieved great successes in data assimilation in atmospheric and oceanic sciences, but its failure in convergence to the right filtering distribution precludes its use for uncertainty quantification. Other existing methods, such as particle filter or sequential importance sampler, do not scale well to the dimension of the system and the sample size of the datasets. In this dissertation, we address these difficulties in a coherent way.</p><p><br></p><p> </p><p>In the first part of the dissertation, we reformulate the EnKF under the framework of Langevin

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