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Uncertainty Quantification and Predictive Computational Science - A Foundation for Physical Scientists and Engineers (Hardcover, 1st ed. 2018) Loot Price: R2,936
Discovery Miles 29 360
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 (Hardcover, 1st ed. 2018)

Ryan G. McClarren

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Loot Price R2,936 Discovery Miles 29 360 | Repayment Terms: R275 pm x 12*

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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.

General

Imprint: Springer International Publishing AG
Country of origin: Switzerland
Release date: December 2018
First published: 2018
Authors: Ryan G. McClarren
Dimensions: 235 x 155 x 24mm (L x W x T)
Format: Hardcover
Pages: 345
Edition: 1st ed. 2018
ISBN-13: 978-3-319-99524-3
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Science & Mathematics > Physics > General
Books > Computing & IT > Applications of computing > Computer modelling & simulation
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling
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LSN: 3-319-99524-3
Barcode: 9783319995243

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