Ghanem R., Higdon D., Owhadi H. (Eds.) Handbook of Uncertainty Quantification (3 Volume Set)

Springer, 2017. — 2035 p. — (Springer Reference). — ISBN: 978-3-319-12384-4.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.Volume 1
IntroductiontoUncertaintyQuantification
Introduction to Uncertainty Quantification
Methodology
Bayes Linear Emulation, History Matching, and Forecasting for Complex Computer Simulators
Inference Given Summary Statistics
Multi-response Approach to Improving Identifiability in Model Calibration
Validation of Physical Models in the Presence of Uncertainty
Toward Machine Wald
Hierarchical Models for Uncertainty Quantification: An Overview
Random Matrix Models and Nonparametric Method for Uncertainty Quantification
Maximin Sliced Latin Hypercube Designs with Application to Cross Validating Prediction Error
The Bayesian Approach to Inverse Problems
Multilevel Uncertainty Integration
Bayesian Cubic Spline in Computer Experiments
Propagation of Stochasticity in Heterogeneous Media and Applications to Uncertainty Quantification
Polynomial Chaos: Modeling, Estimation, and Approximation
Volume 2
ForwardProblems
Bayesian Uncertainty Propagation Using Gaussian Processes
Solution Algorithms for Stochastic Galerkin Discretizations of Differential Equations with Random Data
Intrusive Polynomial Chaos Methods for Forward Uncertainty Propagation
Multiresolution Analysis for Uncertainty
Surrogate Models for Uncertainty Propagation and Sensitivity Analysis
Stochastic Collocation Methods: A Survey
Sparse Collocation Methods for Stochastic Interpolation and Quadrature
Method of Distributions for Uncertainty Quantification
Sampling via Measure Transport: An Introduction
Compressive Sampling Methods for Sparse Polynomial Chaos Expansions
Low-Rank Tensor Methods for Model Order Reduction
Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems
Model Order Reduction Methods in Computational Uncertainty Quantification
Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models
Mori-Zwanzig Approach to Uncertainty Quantification
Rare-Event Simulation
Introduction to Sensitivity Analysis
Introduction to Sensitivity Analysis
Variational Methods
Design of Experiments for Screening
Weights and Importance in Composite Indicators: Mind the Gap
Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms
Derivative-Based Global Sensitivity Measures
Moment-Independent and Reliability-Based Importance Measures
Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes
Sensitivity Analysis of Spatial and/or Temporal Phenomena
Volume 3
Risk
Decision Analytic and Bayesian Uncertainty Quantification for Decision Support
Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries
Robust Design and Uncertainty Quantification for Managing Risks in Engineering
Quantifying and Reducing Uncertainty About Causality in Improving Public Health and Safety
Codes of Practice and Factors of Safety
Conceptual Structure of Performance Assessments for Geologic Disposal of Radioactive Waste
Redundancy of Structures and Fatigue of Bridges and Ships Under Uncertainty
Uncertainty Approaches in Ship Structural Performance
Uncertainty Quantification’s Role in Modeling and Simulation Planning, and Credibility Assessment Through the Predictive Capability Maturity Model
Uncertainty Quantification in a Regulatory Environment
Introduction to Software for Uncertainty Quantification
Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment
Problem Solving Environment for Uncertainty Analysis and Design Exploration
Probabilistic Analysis Using NESSUS (Numerical Evaluation of Stochastic Structures Under Stress)
Embedded Uncertainty Quantification Methods via Stokhos
Uncertainty Quantification Toolkit (UQTk)
The Parallel C++ Statistical Library for Bayesian Inference: QUESO
Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA
COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management
SIMLAB Software for Uncertainty and Sensitivity Analysis
OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation