9783319861814 - numerical methods for stochastic partial differential equations with white noise (applied mathematical sciences, band 196) von zhang, zhongqiang; karniadakis, george em (4 Ergebnisse)
Sprache: Englisch
Verlag: Springer 2018
Serie: Applied Mathematical Sciences, Buch 80 von 94. Buch 80 von 94 - Applied Mathematical Sciences
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Zustand: New. *Price HAS BEEN REDUCED by 10% until Monday, June 22 (weekend SALE item)* 409 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipi…ent's country.
Sprache: Englisch
Verlag: Springer 2018
Serie: Applied Mathematical Sciences, Buch 80 von 94. Buch 80 von 94 - Applied Mathematical Sciences
- Softcover
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Sprache: Englisch
Verlag: Springer 2018
Serie: Applied Mathematical Sciences, Buch 80 von 94. Buch 80 von 94 - Applied Mathematical Sciences
- Softcover
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Taschenbuch. Zustand: Neu. Numerical Methods for Stochastic Partial Differential Equations with White Noise | Zhongqiang Zhang (u. a.) | Taschenbuch | Applied Mathematical Sciences | xv | Englisch | 2018 | Springer | EAN 9783319861814 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg,… juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing 2018
Serie: Applied Mathematical Sciences, Buch 80 von 94. Buch 80 von 94 - Applied Mathematical Sciences
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation. The book begins with some motivational and background material in the introductory chapters and is divi…ded into three parts. Part I covers numerical stochastic ordinary differential equations. Here the authors start with numerical methods for SDEs with delay using the Wong-Zakai approximation and finite difference in time. Part II covers temporal white noise. Here the authors consider SPDEs as PDEs driven by white noise, where discretization of white noise (Brownian motion) leads to PDEs with smooth noise, which can then be treated by numerical methods for PDEs. In this part, recursive algorithms based on Wiener chaos expansion and stochastic collocation methods are presented for linear stochastic advection-diffusion-reaction equations. In addition, stochastic Euler equations are exploited as an application of stochastic collocation methods, where a numerical comparison with other integration methods in random space is made. Part III covers spatial white noise. Here the authors discuss numerical methods for nonlinear elliptic equations as well as other equations with additive noise. Numerical methods for SPDEs with multiplicative noise are also discussed using the Wiener chaos expansion method. In addition, some SPDEs driven by non-Gaussian white noise are discussed and some model reduction methods (based on Wick-Malliavin calculus) are presented for generalized polynomial chaos expansion methods. Powerful techniques are provided for solving stochastic partial differential equations. This book can be considered as self-contained. Necessary background knowledge is presented in the appendices. Basic knowledge of probability theory and stochastic calculus is presented in Appendix A. In Appendix B some semi-analytical methods for SPDEs are presented. In Appendix C an introduction to Gauss quadrature is provided.In Appendix D, all the conclusions which are needed for proofs are presented, and in Appendix E a method to compute the convergence rate empirically is included. In addition, the authors provide a thorough review of the topics, both theoretical and computational exercises in the book with practical discussion of the effectiveness of the methods. Supporting Matlab files are made available to help illustrate some of the concepts further. Bibliographic notes are included at the end of each chapter. This book serves as a reference for graduate students and researchers in the mathematical sciences who would like to understand state-of-the-art numerical methods for stochastic partial differential equations with white noise.
