Uncertainty Quantification And Sensitivity Analysis In Bayesian Inverse Problems PDF Download
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Author | : William McNeil Reese |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : |
ISBN | : |
Download Uncertainty Quantification and Sensitivity Analysis in Bayesian Inverse Problems Book in PDF, ePub and Kindle
Author | : Lorenz Biegler |
Publisher | : Wiley |
Total Pages | : 388 |
Release | : 2010-11-15 |
Genre | : Mathematics |
ISBN | : 9780470697436 |
Download Large-Scale Inverse Problems and Quantification of Uncertainty Book in PDF, ePub and Kindle
This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: • Brings together the perspectives of researchers in areas of inverse problems and data assimilation. • Assesses the current state-of-the-art and identify needs and opportunities for future research. • Focuses on the computational methods used to analyze and simulate inverse problems. • Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
Author | : Johnathan M. Bardsley |
Publisher | : SIAM |
Total Pages | : 135 |
Release | : 2018-08-01 |
Genre | : Science |
ISBN | : 1611975387 |
Download Computational Uncertainty Quantification for Inverse Problems Book in PDF, ePub and Kindle
This book is an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The book also presents more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems. Each chapter contains MATLAB® code that implements the algorithms and generates the figures, as well as a large number of exercises accessible to both graduate students and researchers. Computational Uncertainty Quantification for Inverse Problems is intended for graduate students, researchers, and applied scientists. It is appropriate for courses on computational inverse problems, Bayesian methods for inverse problems, and UQ methods for inverse problems.
Author | : Juan Chiachio-Ruano |
Publisher | : CRC Press |
Total Pages | : 248 |
Release | : 2021-11-11 |
Genre | : Mathematics |
ISBN | : 1351869663 |
Download Bayesian Inverse Problems Book in PDF, ePub and Kindle
This book is devoted to a special class of engineering problems called Bayesian inverse problems. These problems comprise not only the probabilistic Bayesian formulation of engineering problems, but also the associated stochastic simulation methods needed to solve them. Through this book, the reader will learn how this class of methods can be useful to rigorously address a range of engineering problems where empirical data and fundamental knowledge come into play. The book is written for a non-expert audience and it is contributed to by many of the most renowned academic experts in this field.
Author | : Ralph C. Smith |
Publisher | : SIAM |
Total Pages | : 400 |
Release | : 2013-12-02 |
Genre | : Computers |
ISBN | : 1611973228 |
Download Uncertainty Quantification Book in PDF, ePub and Kindle
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 can find data used in the exercises and other supplementary material.
Author | : Erin Camille DeCarlo |
Publisher | : |
Total Pages | : |
Release | : 2017 |
Genre | : Electronic dissertations |
ISBN | : |
Download Uncertainty Quantification and Confidence Assessment in Time-dependent, Multidisciplinary Simulations Book in PDF, ePub and Kindle
Author | : Roger Ghanem |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2016-05-08 |
Genre | : Mathematics |
ISBN | : 9783319123844 |
Download Handbook of Uncertainty Quantification Book in PDF, ePub and Kindle
The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.
Author | : T.J. Sullivan |
Publisher | : Springer |
Total Pages | : 351 |
Release | : 2015-12-14 |
Genre | : Mathematics |
ISBN | : 3319233955 |
Download Introduction to Uncertainty Quantification Book in PDF, ePub and Kindle
This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) 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 favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study. 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 is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
Author | : Guojun Hu |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
Download Inverse Uncertainty Quantification of Trace Physical Model Parameters Using Bayesian Analysis Book in PDF, ePub and Kindle
Author | : National Research Council |
Publisher | : National Academies Press |
Total Pages | : 144 |
Release | : 2012-07-26 |
Genre | : Mathematics |
ISBN | : 0309256348 |
Download Assessing the Reliability of Complex Models Book in PDF, ePub and Kindle
Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners.