Approximation of Stochastic Invariant Manifolds (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2015
- Softcover
- New
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Paperback. This first volume is concerned with the analytic derivation of explicit formulas for the leading-order Taylor approximations of (local) stochastic invariant manifolds associated with a broad class of nonlinear stochastic partial differential equations. These approximations take the form of Lyapunov-Perron integrals, which are further characterized in Volume II as pullback limits associated with some partially coupled backward-forward systems. This pullback characterization provides a useful interpretation of the corresponding approximating manifolds and leads to a simple framework that unifies some other approximation approaches in the literature. A self-contained survey is also included on the existence and attraction of one-parameter families of stochastic invariant manifolds, from the point of view of the theory of random dynamical systems. This first volume is concerned with the analytic derivation of explicit formulas for the leading-order Taylor approximations of (local) stochastic invariant manifolds associated with a broad class of nonlinear stochastic partial differential equations. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
Seller Inventory # 9783319124957
- Title
- Approximation of Stochastic Invariant Manifolds (Paperback)
- Author
- Honghu Liu
- Publisher
- Springer International Publishing AG, Cham
- Publication year
- 2015
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3319124951
- ISBN 13
- 9783319124957
- Series
- Book 52 of 155: SpringerBriefs in Mathematics
"Synopsis" may belong to another edition of this title.
Review
“The book under review is the first in a two-volume series and deals with approximation of stochastic manifolds that are invariant for dynamics of a parabolic Stratonovich SPDE driven by a one-dimensional Wiener process. ... The book is aimed at readers interested in stochastic partial differential equations and random dynamical systems.” (Martin Ondreját, zbMATH 1319.60002, 2015)
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