The Dynamics of Nonlinear Reaction-Diffusion Equations with Small Lévy Noise [electronic resource] /by Arnaud Debussche, Michael Högele, Peter Imkeller.
by Debussche, Arnaud [author.]; Högele, Michael [author.]; Imkeller, Peter [author.]; SpringerLink (Online service).
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BookSeries: Lecture Notes in Mathematics: 2085Publisher: Cham : Springer International Publishing : 2013.Description: XIV, 165 p. 9 illus., 8 illus. in color. online resource.ISBN: 9783319008288.Subject(s): Mathematics | Differentiable dynamical systems | Differential equations, partial | Distribution (Probability theory) | Mathematics | Probability Theory and Stochastic Processes | Dynamical Systems and Ergodic Theory | Partial Differential EquationsDDC classification: 519.2 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| QA274-274.9 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | QA273.A1-274.9 (Browse shelf) | Available |
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Introduction -- The fine dynamics of the Chafee- Infante equation -- The stochastic Chafee- Infante equation -- The small deviation of the small noise solution -- Asymptotic exit times -- Asymptotic transition times -- Localization and metastability -- The source of stochastic models in conceptual climate dynamics.
This work considers a small random perturbation of alpha-stable jump type nonlinear reaction-diffusion equations with Dirichlet boundary conditions over an interval. It has two stable points whose domains of attraction meet in a separating manifold with several saddle points. Extending a method developed by Imkeller and Pavlyukevich it proves that in contrast to a Gaussian perturbation, the expected exit and transition times between the domains of attraction depend polynomially on the noise intensity in the small intensity limit. Moreover the solution exhibits metastable behavior: there is a polynomial time scale along which the solution dynamics correspond asymptotically to the dynamic behavior of a finite-state Markov chain switching between the stable states.
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