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Uncertainty Quantification, 2nd Edition
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Theory, Implementation, and Applications Author: Smith, Ralph C. Publisher: SIAM ISBN: 9781611977837 Cover: HARDCOVER Date: 2024年09月 DESCRIPTION Uncertainty quantification serves a fundamental role when establishing the predictive capabilities of simulation models. This book provides a comprehensive and unified treatment of the mathematical, statistical, and computational theory and methods employed to quantify uncertainties associated with models from a wide range of physical, biological, and engineering applications. Concepts are motivated and illustrated by a large set of examples. This second edition has been significantly revised and expanded to include advances in the field and to provide a comprehensive sensitivity analysis and uncertainty quantification framework for models from science and engineering. Reorganized into five parts, the book covers applications and models; concepts from probability and statistics; parameter identifiability, sensitivity analysis, and active subspace techniques; parameter inference, uncertainty propagation, and model discrepancy; and construction of surrogate and reduced-order models. Highlights of the second edition also include: five new chapters on random field representations, observation models, parameter identifiability and influence, active subspace analysis, and statistical surrogate models; revision and extension of remaining chapters to incorporate methodological advances in sensitivity and uncertainty analysis; over 100 exercises to illustrate basic concepts and guide readers regarding the implementation of algorithms; numerous additional examples, several of which include data; additional applications including pharmacology models, digital twins, virtual populations, radiation detection in an urban environment, and a wetland methane emission model; UQ Crimes listed throughout the text to identify common misconceptions TABLE OF CONTENTS Chapter 1: Introduction Chapter 2: Applications Chapter 3: Models and Data Chapter 4: Topics from Probability and Statistics Chapter 5: Representation of Random Parameters and Fields Chapter 6: Observation Models Chapter 7: Parameter Identifiability and Influence Chapter 8: Local Sensitivity Analysis Chapter 9: Global Sensitivity Analysis Chapter 10: Active Subspace Analysis Chapter 11: Frequentist Parameter Inference Chapter 12: Bayesian Parameter Inference Chapter 13: Uncertainty Propagation for Model Responses Chapter 14: Model Discrepancy Chapter 15: Surrogate Models Chapter 16: Numerical Surrogate Models Chapter 17: Spectral Surrogates for Differential Equations Chapter 18: Statistical Surrogate Models Chapter 19: Reduced-Order Models Chapter 20: Numerical and Statistical Integration Techniques
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