Overview
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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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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Keywords
Table of contents (58 entries)
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Front Matter
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Introduction to Uncertainty Quantification
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Front Matter
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Methodology
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Front Matter
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Reviews
Editors and Affiliations
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.
Bibliographic Information
Book Title: Handbook of Uncertainty Quantification
Editors: Roger Ghanem, David Higdon, Houman Owhadi
DOI: https://doi.org/10.1007/978-3-319-12385-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Reference Module Computer Science and Engineering
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-12384-4Published: 29 June 2017
eBook ISBN: 978-3-319-12385-1Published: 16 June 2017
Edition Number: 1
Number of Pages: XXV, 2053
Number of Illustrations: 96 b/w illustrations, 424 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Mathematical and Computational Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences