Handbook of Uncertainty Quantification

Table of Contents

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   Book Overview
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   Chapter 1 Introduction to Uncertainty Quantification
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   Chapter 2 Validation of Physical Models in the Presence of Uncertainty
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   Chapter 3 Toward Machine Wald
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   Chapter 4 Hierarchical Models for Uncertainty Quantification: An Overview
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   Chapter 5 Random Matrix Models and Nonparametric Method for Uncertainty Quantification
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   Chapter 6 Maximin Sliced Latin Hypercube Designs with Application to Cross Validating Prediction Error
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   Chapter 7 The Bayesian Approach to Inverse Problems
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   Chapter 8 Multilevel Uncertainty Integration
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    Chapter 9 Propagation of Stochasticity in Heterogeneous Media and Applications to Uncertainty Quantification
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    Chapter 13 Polynomial Chaos: Modeling, Estimation, and Approximation
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    Chapter 14 Bayes Linear Emulation, History Matching, and Forecasting for Complex Computer Simulators
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    Chapter 16 Bayesian Uncertainty Propagation Using Gaussian Processes
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    Chapter 18 Multiresolution Analysis for Uncertainty Quantification
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    Chapter 19 Intrusive Polynomial Chaos Methods for Forward Uncertainty Propagation
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    Chapter 20 Solution Algorithms for Stochastic Galerkin Discretizations of Differential Equations with Random Data
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    Chapter 21 Low-Rank Tensor Methods for Model Order Reduction
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    Chapter 22 Surrogate Models for Uncertainty Propagation and Sensitivity Analysis
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    Chapter 23 Sampling via Measure Transport: An Introduction
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    Chapter 24 Rare-Event Simulation
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    Chapter 25 Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models
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    Chapter 26 Stochastic Collocation Methods: A Survey
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    Chapter 27 Method of Distributions for Uncertainty Quantification
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    Chapter 28 Mori-Zwanzig Approach to Uncertainty Quantification
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    Chapter 28 Mori-Zwanzig Approach to Uncertainty Quantification
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    Chapter 29 Sparse Collocation Methods for Stochastic Interpolation and Quadrature
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    Chapter 30 Random Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems
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    Chapter 31 Introduction to Sensitivity Analysis
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    Chapter 32 Variational Methods
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    Chapter 33 Design of Experiments for Screening
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    Chapter 35 Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms
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    Chapter 36 Derivative-Based Global Sensitivity Measures
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    Chapter 37 Moment-Independent and Reliability-Based Importance Measures
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    Chapter 38 Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes
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    Chapter 39 Sensitivity Analysis of Spatial and/or Temporal Phenomena
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    Chapter 40 Weights and Importance in Composite Indicators: Mind the Gap
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    Chapter 41 Decision Analytic and Bayesian Uncertainty Quantification for Decision Support
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    Chapter 44 Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries
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    Chapter 45 Robust Design and Uncertainty Quantification for Managing Risks in Engineering
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    Chapter 46 Conceptual Structure of Performance Assessments for Geologic Disposal of Radioactive Waste
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    Chapter 47 Redundancy of Structures and Fatigue of Bridges and Ships Under Uncertainty
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    Chapter 47 Redundancy of Structures and Fatigue of Bridges and Ships Under Uncertainty
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    Chapter 48 Uncertainty Approaches in Ship Structural Performance
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    Chapter 49 Uncertainty Quantification in a Regulatory Environment
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    Chapter 51 Uncertainty Quantification’s Role in Modeling and Simulation Planning, and Credibility Assessment Through the Predictive Capability Maturity Model
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    Chapter 52 Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment
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    Chapter 53 Problem Solving Environment for Uncertainty Analysis and Design Exploration
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    Chapter 54 Probabilistic Analysis using NESSUS (Numerical Evaluation of Stochastic Structures Under Stress)
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    Chapter 55 Embedded Uncertainty Quantification Methods via Stokhos
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    Chapter 56 Uncertainty Quantification Toolkit (UQTk)
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    Chapter 57 The Parallel C++ Statistical Library for Bayesian Inference: QUESO
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    Chapter 58 Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA
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    Chapter 59 COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management
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    Chapter 61 SIMLAB Software for Uncertainty and Sensitivity Analysis
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    Chapter 64 OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation
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    Chapter 65 Multi-response Approach to Improving Identifiability in Model Calibration
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    Chapter 67 Compressive Sampling Methods for Sparse Polynomial Chaos Expansions
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    Chapter 68 Inference Given Summary Statistics
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    Chapter 69 Bayesian Cubic Spline in Computer Experiments
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    Chapter 70 Model Order Reduction Methods in Computational Uncertainty Quantification
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    Chapter 71 Quantifying and Reducing Uncertainty about Causality in Improving Public Health and Safety