9780521728522-0521728525-An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics, Series Number 50)

An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics, Series Number 50)

ISBN-13: 9780521728522
ISBN-10: 0521728525
Edition: 1
Author: Lord, Gabriel J.
Publication date: 2014
Publisher: Cambridge University Press
Format: Paperback 520 pages
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Book details

ISBN-13: 9780521728522
ISBN-10: 0521728525
Edition: 1
Author: Lord, Gabriel J.
Publication date: 2014
Publisher: Cambridge University Press
Format: Paperback 520 pages

Summary

Acknowledged authors Lord, Gabriel J. wrote An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics, Series Number 50) comprising 520 pages back in 2014. Textbook and eTextbook are published under ISBN 0521728525 and 9780521728522. Since then An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics, Series Number 50) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method. Later chapters apply the theory of random fields to the numerical solution of elliptic PDEs with correlated random data, discuss the Monte Carlo method, and introduce stochastic Galerkin finite-element methods. Finally, stochastic parabolic PDEs are developed. Assuming little previous exposure to probability and statistics, theory is developed in tandem with state-of the art computational methods through worked examples, exercises, theorems and proofs. The set of MATLAB codes included (and downloadable) allows readers to perform computations themselves and solve the test problems discussed. Practical examples are drawn from finance, mathematical biology, neuroscience, fluid flow modeling and materials science.

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