Uncertainty Quantification and Predictive Computational Science

A Foundation for Physical Scientists and Engineers

Autor: Ryan McClarren

Jazyk:

Vazba: Pevná

Vydavatel: Springer International Publishing AG

Dostupnost: Skladem u dodavatele

Odesíláme za 10-13 dnů

2 234 Kč

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

Informace o knize

Autor

Ryan McClarren

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2018

Stránek

345

EAN

9783319995243

ISBN

3319995243

Enbook ID

19845377

Vydavatel

Springer International Publishing AG

Hmotnost

698

Rozměry

243 x 164 x 24

Kompletní 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.

Mohlo by vás zajímat

Zákaznicí kteří koupili tuto knihu koupili také

Nejčastěji hledané

Categories

Authors

Publishers

Produkty

Produkty

Nejčastěji hledané

Categories

Authors

Publishers

Produkty

Produkty

Uncertainty Quantification and Predictive Computational Science

A Foundation for Physical Scientists and Engineers

Informace o knize

Kompletní popis

Mohlo by vás zajímat

Zákaznicí kteří koupili tuto knihu koupili také