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Handbook of Uncertainty Quantification

  • Reference work
  • © 2017

Overview

Editors:
  1. Roger Ghanem

    1. Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. David Higdon

    1. Social and Decision Analytics Laboratory, Virginia Bioinformatics Institute, Virginia Tech University, Arlington, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Houman Owhadi

    1. Computing and Mathematical Sciences, California Institute of Technology, Pasadena, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Shares cutting edge Uncertainty Quantification ideas with a wide audience

  • Overviews fundamental challenges, applications, and emerging results

  • Draws together the work of mathematicians, statisticians, and engineers

  • Opens the work of top international researchers through an accessible reference work

  • Includes supplementary material: sn.pub/extras

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Table of contents (58 entries)

  1. Front Matter

    Pages i-xxv
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  2. Introduction to Uncertainty Quantification

    1. Front Matter

      Pages 1-1
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    2. Introduction to Uncertainty Quantification

      • Roger Ghanem, David Higdon, Houman Owhadi
      Pages 3-6

  3. Methodology

    1. Front Matter

      Pages 7-7
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    2. Bayes Linear Emulation, History Matching, and Forecasting for Complex Computer Simulators

      • Michael Goldstein, Nathan Huntley
      Pages 9-32

    3. Inference Given Summary Statistics

      • Habib N. Najm, Kenny Chowdhary
      Pages 33-67

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

      • Zhen Jiang, Paul D. Arendt, Daniel W. Apley, Wei Chen
      Pages 69-127

    5. Validation of Physical Models in the Presence of Uncertainty

      • Robert D. Moser, Todd A. Oliver
      Pages 129-156

    6. Toward Machine Wald

      • Houman Owhadi, Clint Scovel
      Pages 157-191

    7. Hierarchical Models for Uncertainty Quantification: An Overview

      • Christopher K. Wikle
      Pages 193-218

    8. Random Matrix Models and Nonparametric Method for Uncertainty Quantification

      • Christian Soize
      Pages 219-287

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

      • Yan Chen, David M. Steinberg, Peter Qian
      Pages 289-309

    10. The Bayesian Approach to Inverse Problems

      • Masoumeh Dashti, Andrew M. Stuart
      Pages 311-428

    11. Multilevel Uncertainty Integration

      • Sankaran Mahadevan, Shankar Sankararaman, Chenzhao Li
      Pages 429-475

    12. Bayesian Cubic Spline in Computer Experiments

      • Yijie Dylan Wang, C. F. Jeff Wu
      Pages 477-495

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

      • Guillaume Bal
      Pages 497-519

    14. Polynomial Chaos: Modeling, Estimation, and Approximation

      • Roger Ghanem, John Red-Horse
      Pages 521-551

  4. Forward Problems

    1. Front Matter

      Pages 553-553
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    2. Bayesian Uncertainty Propagation Using Gaussian Processes

      • Ilias Bilionis, Nicholas Zabaras
      Pages 555-599

    3. Solution Algorithms for Stochastic Galerkin Discretizations of Differential Equations with Random Data

      • Howard Elman
      Pages 601-616
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Keywords

About this book

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.      

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Editors and Affiliations

  • Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, USA

    Roger Ghanem

  • Social and Decision Analytics Laboratory, Virginia Bioinformatics Institute, Virginia Tech University, Arlington, USA

    David Higdon

  • Computing and Mathematical Sciences, California Institute of Technology, Pasadena, USA

    Houman Owhadi

About the editors

Roger Ghanem is the Gordon S. Marshall Professor of Engineering at the University of Southern California where he holds joint appointments in the Departments of Civil & Environmental Engineering and Mechanical & Aerospace Engineering.

David Higdon is Scientists and Group Leader in Statistical Sciences at Los Alamos National Laboratories. He has developed statistical concepts and methodologies that are uniquely adapted to modeling and simulation and computationally intensive numerical models

Houman Owhadi is a Professor of Applied & Computational Mathematics and Control & Dynamical Systems at the California Institute of Technology.    

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