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
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