Handbook of Uncertainty Quantification

Edited by Roger Ghanem (), David Higdon () and Houman Owhadi ()

in Springer Books from Springer

Date: 2017
ISBN: 978-3-319-12385-1
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Chapters in this book:

Ch 1 Introduction to Uncertainty Quantification

Roger Ghanem, David Higdon and Houman Owhadi

Ch 2 Bayes Linear Emulation, History Matching, and Forecasting for Complex Computer Simulators

Michael Goldstein and Nathan Huntley

Ch 3 Inference Given Summary Statistics

Habib N. Najm and Kenny Chowdhary

Ch 4 Multi-response Approach to Improving Identifiability in Model Calibration

Zhen Jiang, Paul D. Arendt, Daniel W. Apley and Wei Chen

Ch 5 Validation of Physical Models in the Presence of Uncertainty

Robert D. Moser and Todd A. Oliver

Ch 6 Toward Machine Wald

Houman Owhadi and Clint Scovel

Ch 7 Hierarchical Models for Uncertainty Quantification: An Overview

Christopher K. Wikle

Ch 8 Random Matrix Models and Nonparametric Method for Uncertainty Quantification

Christian Soize

Ch 9 Maximin Sliced Latin Hypercube Designs with Application to Cross Validating Prediction Error

Yan Chen, David M. Steinberg and Peter Qian

Ch 10 The Bayesian Approach to Inverse Problems

Masoumeh Dashti and Andrew M. Stuart

Ch 11 Multilevel Uncertainty Integration

Sankaran Mahadevan, Shankar Sankararaman and Chenzhao Li

Ch 12 Bayesian Cubic Spline in Computer Experiments

Yijie Dylan Wang and C. F. Jeff Wu

Ch 13 Propagation of Stochasticity in Heterogeneous Media and Applications to Uncertainty Quantification

Guillaume Bal

Ch 14 Polynomial Chaos: Modeling, Estimation, and Approximation

Roger Ghanem and John Red-Horse

Ch 15 Bayesian Uncertainty Propagation Using Gaussian Processes

Ilias Bilionis and Nicholas Zabaras

Ch 16 Solution Algorithms for Stochastic Galerkin Discretizations of Differential Equations with Random Data

Howard Elman

Ch 17 Intrusive Polynomial Chaos Methods for Forward Uncertainty Propagation

Bert Debusschere

Ch 18 Multiresolution Analysis for Uncertainty Quantification

Olivier P. Le Maı̂tre and Omar M. Knio

Ch 19 Surrogate Models for Uncertainty Propagation and Sensitivity Analysis

Khachik Sargsyan

Ch 20 Stochastic Collocation Methods: A Survey

Dongbin Xiu

Ch 21 Sparse Collocation Methods for Stochastic Interpolation and Quadrature

Max Gunzburger, Clayton G. Webster and Guannan Zhang

Ch 22 Method of Distributions for Uncertainty Quantification

Daniel M. Tartakovsky and Pierre A. Gremaud

Ch 23 Sampling via Measure Transport: An Introduction

Youssef Marzouk, Tarek Moselhy, Matthew Parno and Alessio Spantini

Ch 24 Compressive Sampling Methods for Sparse Polynomial Chaos Expansions

Jerrad Hampton and Alireza Doostan

Ch 25 Low-Rank Tensor Methods for Model Order Reduction

Anthony Nouy

Ch 26 Random Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems

Christian Soize

Ch 27 Model Order Reduction Methods in Computational Uncertainty Quantification

Peng Chen and Christoph Schwab

Ch 28 Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models

Michael S. Eldred, Leo W. T. Ng, Matthew F. Barone and Stefan P. Domino

Ch 29 Mori-Zwanzig Approach to Uncertainty Quantification

Daniele Venturi, Heyrim Cho and George Em Karniadakis

Ch 30 Rare-Event Simulation

James L. Beck and Konstantin M. Zuev

Ch 31 Introduction to Sensitivity Analysis

Bertrand Iooss and Andrea Saltelli

Ch 32 Variational Methods

Maelle Nodet and Arthur Vidard

Ch 33 Design of Experiments for Screening

David C. Woods and Susan M. Lewis

Ch 34 Weights and Importance in Composite Indicators: Mind the Gap

William Becker, Paolo Paruolo, Michaela Saisana and Andrea Saltelli

Ch 35 Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms

Clémentine Prieur and Stefano Tarantola

Ch 36 Derivative-Based Global Sensitivity Measures

Sergey Kucherenko and Bertrand Iooss

Ch 37 Moment-Independent and Reliability-Based Importance Measures

Emanuele Borgonovo and Bertrand Iooss

Ch 38 Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes

Loïc Le Gratiet, Stefano Marelli and Bruno Sudret

Ch 39 Sensitivity Analysis of Spatial and/or Temporal Phenomena

Amandine Marrel, Nathalie Saint-Geours and Matthias De Lozzo

Ch 40 Decision Analytic and Bayesian Uncertainty Quantification for Decision Support

D. Warner North

Ch 41 Validation, Verification, and Uncertainty Quantification for Models with Intelligent Adversaries

Jing Zhang and Jun Zhuang

Ch 42 Robust Design and Uncertainty Quantification for Managing Risks in Engineering

Ron Bates

Ch 43 Quantifying and Reducing Uncertainty About Causality in Improving Public Health and Safety

Louis Anthony Cox

Ch 44 Conceptual Structure of Performance Assessments for Geologic Disposal of Radioactive Waste

Jon C. Helton, Clifford W. Hansen and Cédric J. Salaberry

Ch 45 Redundancy of Structures and Fatigue of Bridges and Ships Under Uncertainty

Dan M. Frangopol, Benjin Zhu and Mohamed Soliman

Ch 46 Uncertainty Approaches in Ship Structural Performance

Matthew Collette

Ch 47 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

Ch 48 Uncertainty Quantification in a Regulatory Environment

Vincent A. Mousseau and Brian J. Williams

Ch 49 Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment

Laura P. Swiler, Michael S. Eldred and Brian M. Adams

Ch 50 Problem Solving Environment for Uncertainty Analysis and Design Exploration

Charles Tong

Ch 51 Probabilistic Analysis Using NESSUS (Numerical Evaluation of Stochastic Structures Under Stress)

John M. McFarland and David S. Riha

Ch 52 Embedded Uncertainty Quantification Methods via Stokhos

Eric T. Phipps and Andrew G. Salinger

Ch 53 Uncertainty Quantification Toolkit (UQTk)

Bert Debusschere, Khachik Sargsyan, Cosmin Safta and Kenny Chowdhary

Ch 54 The Parallel C++ Statistical Library for Bayesian Inference: QUESO

Damon McDougall, Nicholas Malaya and Robert D. Moser

Ch 55 Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA

James Gattiker, Kary Myers, Brian J. Williams, Dave Higdon, Marcos Carzolio and Andrew Hoegh

Ch 56 COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management

Edoardo Patelli

Ch 57 SIMLAB Software for Uncertainty and Sensitivity Analysis

Stefano Tarantola and William Becker

Ch 58 OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation

Michaël Baudin, Anne Dutfoy, Bertrand Iooss and Anne-Laure Popelin

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprbok:978-3-319-12385-1

Ordering information: This item can be ordered from
http://www.springer.com/9783319123851

DOI: 10.1007/978-3-319-12385-1

Access Statistics for this book

More books in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().