Table of contents

  1. Yijie Dylan Wang, C. F. Jeff Wu
  2. Sergey Kucherenko, Bertrand Iooss
  3. David C. Woods, Susan M. Lewis
  4. Eric T. Phipps, Andrew G. Salinger
  5. James Gattiker, Kary Myers, Brian Williams, Dave Higdon, Marcos Carzolio, Andrew Hoegh
  6. Habib Najm, Kenny Chowdhary
  7. Bertrand Iooss, Andrea Saltelli
  8. Roger Ghanem, David Higdon, Houman Owhadi

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.


Polynomial Chaos Risk Analysis Risk Models Sensitivity Analysis Uncertainty Quantification

Editors and affiliations

  • Roger Ghanem
    • 1
  • David Higdon
    • 2
  • Houman Owhadi
    • 3
  1. 1.Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Los Alamos National LaboratoryLos AlamosUSA
  3. 3.California Institute of Technology PasadenaUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Online ISBN 978-3-319-11259-6