An Introduction to Computational Stochastic PDEs

An Introduction to Computational Stochastic PDEs

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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 (R) 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 modelling and materials science.
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Product details

  • Electronic book text
  • Cambridge University Press (Virtual Publishing)
  • Cambridge, United Kingdom
  • 107 b/w illus. 16 colour illus. 222 exercises
  • 1139898132
  • 9781139898133

Table of contents

Part I. Deterministic Differential Equations: 1. Linear analysis; 2. Galerkin approximation and finite elements; 3. Time-dependent differential equations; Part II. Stochastic Processes and Random Fields: 4. Probability theory; 5. Stochastic processes; 6. Stationary Gaussian processes; 7. Random fields; Part III. Stochastic Differential Equations: 8. Stochastic ordinary differential equations (SODEs); 9. Elliptic PDEs with random data; 10. Semilinear stochastic PDEs.
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Review quote

'This book gives both accessible and extensive coverage on stochastic partial differential equations and their numerical solutions. It offers a well-elaborated background needed for solving numerically stochastic PDEs, both parabolic and elliptic. For the numerical solutions it presents not only proofs of convergence results of different numerical methods but also actual implementations, here in Matlab, with technical details included ... With numerical implementations hard to find elsewhere in the literature, and a nice presentation of new research findings together with rich references, the book is a welcome companion for anyone working on numerical solutions of stochastic PDEs, and may also be suitable for use in a course on computational stochastic PDEs.' Roger Pettersson, Mathematical Reviews
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About Gabriel J. Lord

Gabriel Lord is a Professor in the Maxwell Institute, Department of Mathematics, at Heriot-Watt University, Edinburgh. He has worked on stochastic PDEs and applications for the past ten years. He is the co-editor of Stochastic Methods in Neuroscience with C. Liang, has organised a number of international meetings in the field, and is principal investigator on the porous media processes and mathematics network funded by the Engineering and Physical Sciences Research Council (UK). He is a member of the Society for Industrial and Applied Mathematics, LMS, and EMS, as well as an Associate Editor for the SIAM Journal on Scientific Computing and the SIAM/ASA Journal on Uncertainty Quantification. Catherine Powell is a Senior Lecturer in Applied Mathematics and Numerical Analysis at the University of Manchester. She has worked in the field of stochastic PDEs and uncertainty quantification for ten years. She has co-organised several conferences on the subject, and together with Tony Shardlow, initialised the annual NASPDE series of meetings (now in its sixth year). Currently, she is the principal investigator on an Engineering and Physical Sciences Research Council funded project on the 'Numerical Analysis of PDEs with Random Data'. She is a member of the Society for Industrial and Applied Mathematics and an Associate Editor for the SIAM/ASA Journal on Uncertainty Quantification. Tony Shardlow has been working in the numerical analysis group at the University of Bath since 2012. Before that, he held appointments at the universities of Manchester, Durham, Oxford, and Minnesota. He completed his Ph.D. in Scientific Computing and Computational Mathematics at Stanford University in 1997.
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