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

Format:

Book

Author/Creator:

McClarren, Ryan G., Author.

Language:

English

Subjects (All):

Physics.

Computer science--Mathematics.

Computer science.

Statistics.

Applied mathematics.

Engineering mathematics.

Mathematical physics.

Computer simulation.

Numerical and Computational Physics, Simulation.

Computational Science and Engineering.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical and Computational Engineering.

Mathematical Applications in the Physical Sciences.

Simulation and Modeling.

Local Subjects:

Numerical and Computational Physics, Simulation.

Computational Science and Engineering.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical and Computational Engineering.

Mathematical Applications in the Physical Sciences.

Simulation and Modeling.

Physical Description:

1 online resource (XVII, 345 p. 141 illus., 99 illus. in color.)

Edition:

1st ed. 2018.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

Summary:

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 R and 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 first year 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. Organizes interdisciplinary topics of uncertainty quantification 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.

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.

ISBN:

3-319-99525-1