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An Introduction to computational sto...

Powell, Catherine E.

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An Introduction to computational stochastic PDEs /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An Introduction to computational stochastic PDEs // Gabriel J. Lord, Catherine E. Powell, Tony Shardlow.
其他題名:	Introduction to computational stochastic partial differential equations
作者:	Lord, Gabriel J.
其他作者:	Powell, Catherine E.
出版者:	New York, NY :Cambridge University Press, : 2014.,
面頁冊數:	xi, 503 p. :ill. (some col.) ;25 cm.
標題:	Stochastic partial differential equations. -
ISBN:	9780521728522

An Introduction to computational stochastic PDEs /
Lord, Gabriel J.

An Introduction to computational stochastic PDEs /Introduction to computational stochastic partial differential equationsGabriel J. Lord, Catherine E. Powell, Tony Shardlow. - New York, NY :Cambridge University Press,2014. - xi, 503 p. :ill. (some col.) ;25 cm. - Cambridge texts in applied mathematics.

Includes bibliographical references (p. [489]-498) and index.

Linear analysis -- Deterministic differential equations --

"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.

ISBN: 9780521728522US60.00

LCCN: 2014005535Subjects--Topical Terms:

625093
Stochastic partial differential equations.

LC Class. No.: QA274.25 / .L67 2014

Dewey Class. No.: 519.2/2

An Introduction to computational stochastic PDEs /
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020 $a 9780521728522 $q (pbk.) : $c US60.00
020 $a 0521728525 $q (pbk.)
020 $a 9780521899901 $q (hbk.)
020 $a 0521899907 $q (hbk.)
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050 0 0 $a QA274.25 $b .L67 2014
082 0 0 $a 519.2/2 $2 23
100 1 $a Lord, Gabriel J. $3 1091416
245 1 0 $a An Introduction to computational stochastic PDEs / $c Gabriel J. Lord, Catherine E. Powell, Tony Shardlow.
246 3 # $a Introduction to computational stochastic partial differential equations
260 # $a New York, NY : $b Cambridge University Press, $c 2014.
300 $a xi, 503 p. : $b ill. (some col.) ; $c 25 cm.
490 1 0 $a Cambridge texts in applied mathematics
504 $a Includes bibliographical references (p. [489]-498) and index.
505 0 # $g Deterministic differential equations -- $t Linear analysis -- $t alerkin approximation and finite elements -- $t Time-dependent differential equations -- $g Stochastic pocesses and random fields -- $t Probability theory -- $t Stochastic processes -- $t Stationary Gaussian processes -- $t Random fields -- $g Stochastic Differential Equations -- $t Stochastic ordinary differential equations (SODEs -- $t Elliptic PDEs with random da -- $t . Semilinear stochastic PDEs.
520 # $a "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.
650 # 0 $a Stochastic partial differential equations. $3 625093
700 1 # $a Powell, Catherine E. $3 2144365
700 1 # $a Shardlow, Tony. $3 2144366