### Abstract

Original language | English |
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Place of Publication | New York, U. S. A. |

Publisher | Cambridge University Press |

Number of pages | 509 |

Edition | 1 |

ISBN (Print) | 9780521899901 |

DOIs | |

Publication status | Published - 1 Jul 2014 |

### Publication series

Name | Cambridge Texts in Applied Mathematics |
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No. | 50 |

### Fingerprint

### Cite this

*Introduction to Computational Stochastic PDEs*. (1 ed.) (Cambridge Texts in Applied Mathematics; No. 50). New York, U. S. A.: Cambridge University Press. https://doi.org/10.1017/CBO9781139017329

**Introduction to Computational Stochastic PDEs.** / Lord, Gabriel J.; Powell, Catherine E.; Shardlow, Tony.

Research output: Book/Report › Book

*Introduction to Computational Stochastic PDEs*. Cambridge Texts in Applied Mathematics, no. 50, 1 edn, Cambridge University Press, New York, U. S. A. https://doi.org/10.1017/CBO9781139017329

}

TY - BOOK

T1 - Introduction to Computational Stochastic PDEs

AU - Lord, Gabriel J.

AU - Powell, Catherine E.

AU - Shardlow, Tony

PY - 2014/7/1

Y1 - 2014/7/1

N2 - 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.

AB - 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.

U2 - 10.1017/CBO9781139017329

DO - 10.1017/CBO9781139017329

M3 - Book

SN - 9780521899901

T3 - Cambridge Texts in Applied Mathematics

BT - Introduction to Computational Stochastic PDEs

PB - Cambridge University Press

CY - New York, U. S. A.

ER -