Uncertainty Quantification and Predictive Computational Science

A Foundation for Physical Scientists and Engineers

Autor: Ryan McClarren

Jazyk:

Väzba: Pevná

Vydavateľ: Springer International Publishing AG

Dostupnosť: Skladom u dodávateľa

Odosielame za 10-13 dní

97.74 €

This textbook teaches the essential background and skills for understanding and quantifying uncertai...

Informácie o knihe

Autor

Ryan McClarren

Jazyk

Angličtina

Väzba

Kniha - Pevná

Vydalo

2018

Stránok

345

EAN

9783319995243

ISBN

3319995243

Enbook ID

19845377

Vydavateľ

Springer International Publishing AG

Hmotnosť

698

Rozmery

243 x 164 x 24

Kompletný popis

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 Scienc e 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.

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Uncertainty Quantification and Predictive Computational Science

A Foundation for Physical Scientists and Engineers

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