Handbook of Uncertainty Quantification

Cham

2017

3 Bände; Vol. 1: S. 1-551 ; Vol. 2: 552-1357 ; Vol. 3: S. 1358-2053

Monographie

978-3-319-12384-4

201860

This UQ Handbook comes at a time when predictive science is emerging at the confluence of computational, physical, and mathematical sciences. Although UQ appears to be an umbrella term, perhaps due in part to the breadth, novelty, and interdisciplinary nature of the field, it is driven by real challenges of practical importance involving the interplay between physics, modeling, computational hardware, algorithmic complexity, and decisions. This Handbook provides a glimpse into these new frontiers of uncertainty quantification. This project was a collaborative effort. The section editors, Drs. Wei Cen, Habib Najm, Bertrand Iooss, Tony Cox, Jim Stewart, and Michael McKerns, provided their own perspective on the Handbook’s content and are responsible for both its breadth and cohesion. The editorial staff at Springer provided professional and insightful assistance with diligence and patience. The Handbook would not have been possible without the authors who did all the heavy lifting. We are grateful for their hard work and their confidence.
Wahrscheinlichkeitstheorie, Stochastische Prozesse
Part I IntroductiontoUncertaintyQuantification ; Introduction to Uncertainty Quantification ;
Roger Ghanem, David Higdon, and Houman Owhadi; Part II Methodology ; Bayes Linear Emulation, History Matching, and Forecasting for Complex Computer Simulators ; Michael Goldstein and Nathan Huntley; Inference Given Summary Statistics ; Habib N. Najm and Kenny Chowdhary; Multi-response Approach to Improving Identifiability in Model Calibration ; Zhen Jiang, Paul D. Arendt, Daniel W. Apley, and Wei Chen; Validation of Physical Models in the Presence of Uncertainty ; Robert D. Moser and Todd A. Oliver; Toward Machine Wald ; ; Houman Owhadi and Clint Scovel; Hierarchical Models for Uncertainty Quantification: An Overview ; Christopher K. Wikle; Random Matrix Models and Nonparametric Method for Uncertainty Quantification ; Christian Soize; Maximin Sliced Latin Hypercube Designs with Application to Cross Validating Prediction Error ; Yan Chen, David M. Steinberg, and Peter Qian; The Bayesian Approach to Inverse Problems ; Masoumeh Dashti and Andrew M. Stuart; Multilevel Uncertainty Integration ; Sankaran Mahadevan, Shankar Sankararaman, Chenzhao Li ; Bayesian Cubic Spline in Computer Experiments ; Yijie Dylan Wang and C. F. Jeff Wu; Propagation of Stochasticity in Heterogeneous Media and Applications to Uncertainty Quantification ; Guillaume Bal; Polynomial Chaos: Modeling, Estimation, and Approximation ; Roger Ghanem and John Red-Horse; Part III Forward Problems ; Bayesian Uncertainty Propagation Using Gaussian Processes ; Ilias Bilionis and Nicholas Zabaras; Solution Algorithms for Stochastic Galerkin Discretizations of Differential Equations with Random Data ; Howard Elman; Intrusive Polynomial Chaos Methods for Forward Uncertainty Propagation ; Bert Debusschere; Multiresolution Analysis for Uncertainty Quantification ; Olivier P. Le Maître and Omar M. Knio; Surrogate Models for Uncertainty Propagation and Sensitivity Analysis ;
Khachik Sargsyan; Stochastic Collocation Methods: A Survey ; Dongbin Xiu; Sparse Collocation Methods for Stochastic Interpolation and Quadrature ; Max Gunzburger, Clayton G. Webster, and Guannan Zhang; Method of Distributions for Uncertainty Quantification ; Daniel M. Tartakovsky and Pierre A. Gremaud; Sampling via Measure Transport: An Introduction ; Youssef Marzouk, Tarek Moselhy, Matthew Parno, Alessio Spantini; Compressive Sampling Methods for Sparse Polynomial Chaos Expansions ; Jerrad Hampton and Alireza Doostan; Low-Rank Tensor Methods for Model Order Reduction ; Anthony Nouy; Random Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems ; Christian Soize; Model Order Reduction Methods in Computational Uncertainty Quantification ; Peng Chen and Christoph Schwab; Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models ; Michael S. Eldred, Leo W. T. Ng, Matthew F. Barone, Stefan P. Domino; Mori-Zwanzig Approach to Uncertainty Quantification ; Daniele Venturi, Heyrim Cho, and George Em Karniadakis; Rare-Event Simulation ; James L. Beck and Konstantin M. Zuev; Part IV Introduction to Sensitivity Analysis ; 31 Introduction to Sensitivity Analysis ; Bertrand Iooss; Andrea Saltelli; Variational Methods ; Maelle Nodet and Arthur Vidard; Design of Experiments for Screening ; David C. Woods and Susan M. Lewis; Weights and Importance in Composite Indicators: Mind the Gap ; William Becker, Paolo Paruolo, Michaela Saisana, Andrea Saltelli; Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms ; Clémentine Prieur and Stefano Tarantola; Derivative-Based Global Sensitivity Measures ; Sergey Kucherenko and Bertrand Iooss; Moment-Independent and Reliability-Based Importance Measures ; Emanuele Borgonovo and Bertrand Iooss; Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes ; Loïc Le Gratiet, Stefano Marelli, Bruno Sudret; Sensitivity Analysis of Spatial and/or Temporal Phenomena ; Amandine Marrel, Nathalie Saint-Geours, Matthias De Lozzo; Part V Risk ; Decision Analytic and Bayesian Uncertainty Quantification for Decision Support ; D. Warner North; Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries ; Jing Zhang and Jun Zhuang; Robust Design and Uncertainty Quantification for Managing Risks in Engineering ; Ron Bates; Quantifying and Reducing Uncertainty About Causality in Improving Public Health and Safety ; Louis Anthony Cox, Jr.; Part VI Codes of Practice and Factors of Safety ; Conceptual Structure of Performance Assessments for Geologic Disposal of Radioactive Waste ; Jon C. Helton, Clifford W. Hansen, and Cédric J. Salaberry; Redundancy of Structures and Fatigue of Bridges and Ships Under Uncertainty ; Dan M. Frangopol, Benjin Zhu, and Mohamed Soliman; Uncertainty Approaches in Ship Structural Performance ; Matthew Collette ; Uncertainty Quantification’s Role in Modeling and Simulation Planning, and Credibility Assessment Through the Predictive Capability Maturity Model ; W. J. Rider, W. R. Witkowski, and Vincent A. Mousseau; Uncertainty Quantification in a Regulatory Environment ; Vincent A. Mousseau ; Brian J. Williams; Part VII Introduction to Software for Uncertainty Quantification ; Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment ; Laura P. Swiler, Michael S. Eldred, and Brian M. Adams; Problem Solving Environment for Uncertainty Analysis and Design Exploration ; Charles Tong; Probabilistic Analysis Using NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) ; John M. McFarland and David S. Riha; Embedded Uncertainty Quantification Methods via Stokhos ; Eric T. Phipps and Andrew G. Salinger; Uncertainty Quantification Toolkit (UQTk) ; Bert Debusschere, Khachik Sargsyan, Cosmin Safta, Kenny Chowdhary; The Parallel C++ Statistical Library for Bayesian Inference: QUESO ; Damon McDougall, Nicholas Malaya, and Robert D. Moser; Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA ; James Gattiker, Kary Myers, Brian J. Williams, Dave Higdon, Marcos Carzolio, Andrew Hoegh; COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management ; Edoardo Patelli; SIMLAB Software for Uncertainty and Sensitivity Analysis ; Stefano Tarantola ; William Becker; OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation ; Michaël Baudin, Anne Dutfoy, Bertrand Iooss, Anne-Laure Popelin