Overview
- Presents fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification
- Includes several topics not currently published in research monographs
- Covers the basic models and advanced methodologies for constructing the stochastic modeling of uncertainties
Part of the book series: Interdisciplinary Applied Mathematics (IAM, volume 47)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
- High Stochastic Dimension
- Maximum Entropy Principle
- MCMC Methods
- Model Uncertainties
- Model-parameter Uncertainties
- Non-Gaussian Random Fields
- Nonparametric Uncertainties
- Polynomial Chaos Expansion
- Random Matrices
- Robust Design
- Statistical Inverse Problems
- Stochastic Reduced-order Computational Models
About this book
Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available.
This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.
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Bibliographic Information
Book Title: Uncertainty Quantification
Book Subtitle: An Accelerated Course with Advanced Applications in Computational Engineering
Authors: Christian Soize
Series Title: Interdisciplinary Applied Mathematics
DOI: https://doi.org/10.1007/978-3-319-54339-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-54338-3Published: 03 May 2017
Softcover ISBN: 978-3-319-85372-7Published: 25 July 2018
eBook ISBN: 978-3-319-54339-0Published: 24 April 2017
Series ISSN: 0939-6047
Series E-ISSN: 2196-9973
Edition Number: 1
Number of Pages: XXII, 329
Number of Illustrations: 24 b/w illustrations, 86 illustrations in colour
Topics: Computational Science and Engineering, Mathematical and Computational Engineering, Probability Theory and Stochastic Processes