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Uncertainty Quantification and Predictive Computational Science

A Foundation for Physical Scientists and Engineers

Authors: McClarren, Ryan

  • Organizes wide-ranging and interdisciplinary topics of uncertainty quantification from multiple sources into a single teaching text
  • Reviews the fundamentals of probability and statistics
  • Guides the transition from merely performing calculations to making confident predictions
  • Builds readers’ confidence in the validity of their simulations
  • Illustrates concepts with real-world examples and models from the physical sciences and engineering
  • Includes R and python code, enabling readers to perform the analysis under discussion
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Buy this book

eBook $79.99
price for USA in USD (gross)
  • ISBN 978-3-319-99525-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.99
price for USA in USD
  • ISBN 978-3-319-99524-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences.

Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment.

The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems.  

Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

About the authors

Ryan McClarren has been teaching uncertainty quantification and predictive computational science to students from various engineering and physical science departments at since 2009. He is currently Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame. Prior to joining Notre Dame in 2017, he was Assistant Professor of Nuclear Engineering at Texas A&M University, an institution well-known in the nuclear engineering community for its computational research and education. He has authored numerous publications in refereed journals, is the author of a book that teaches python and numerical methods to undergraduates, Computational Nuclear Engineering and Radiological Science Using Python, and was the editor of a special issue of the journal Transport Theory and Statistical Physics. A well-known member of the computational nuclear engineering community, he has won research awards from NSF, DOE, and three national labs. While an undergraduate at the University of Michigan he won three awards for creative writing. Before joining the faculty of Texas A&M, Dr. McClarren was a research scientist at Los Alamos National Laboratory in the Computational Physics and Methods group.

Table of contents (12 chapters)

  • Introduction to Uncertainty Quantification and Predictive Science

    McClarren, Ryan G.

    Pages 3-17

  • Probability and Statistics Preliminaries

    McClarren, Ryan G.

    Pages 19-51

  • Input Parameter Distributions

    McClarren, Ryan G.

    Pages 53-91

  • Local Sensitivity Analysis Based on Derivative Approximations

    McClarren, Ryan G.

    Pages 95-109

  • Regression Approximations to Estimate Sensitivities

    McClarren, Ryan G.

    Pages 111-128

Buy this book

eBook $79.99
price for USA in USD (gross)
  • ISBN 978-3-319-99525-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.99
price for USA in USD
  • ISBN 978-3-319-99524-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Uncertainty Quantification and Predictive Computational Science
Book Subtitle
A Foundation for Physical Scientists and Engineers
Authors
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-319-99525-0
DOI
10.1007/978-3-319-99525-0
Hardcover ISBN
978-3-319-99524-3
Edition Number
1
Number of Pages
XVII, 345
Number of Illustrations
42 b/w illustrations, 99 illustrations in colour
Topics