Uncertainty Quantification and Predictive Computational Science A Foundation for Physical Scientists and Engineers

Uncertainty Quantification and Predictive Computational Science A Foundation for Physical Scientists and Engineers / by Ryan G. McClarren.

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-conse...

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Bibliographic Details
Main Author: McClarren, Ryan G. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
Edition:1st ed. 2018.
Series:Springer eBook Collection.
Subjects:
Physics.
Computer mathematics.
Statistics .
Applied mathematics.
Engineering mathematics.
Mathematical physics.
Computer simulation.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Part I Fundamentals
  • Introduction
  • Probability and Statistics Preliminaries
  • Input Parameter Distributions
  • Part II Local Sensitivity Analysis
  • Derivative Approximations
  • Regression Approximations
  • Adjoint-based Local Sensitivity Analysis
  • Part III Parametric Uncertainty Quantification
  • From Sensitivity Analysis to UQ
  • Sampling-Based UQ
  • Reliability Methods
  • Polynomial Chaos Methods
  • Part IV Predictive Science
  • Emulators and Surrogate Models
  • Reduced Order Models
  • Predictive Models
  • Epistemic Uncertainties
  • Appendices
  • A. A cookbook of distributions.

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