title: Identifying almost invariant sets in stochastic dynamical systems.
abstract: We consider the approximation of fluctuation induced almost invariant sets arising from stochastic dynamical systems. The dynamical evolution of densities is derived from the stochastic Frobenius-Perron operator. Given a stochastic kernel with a known distribution, approximate almost invariant sets are found by translating the problem into an eigenvalue problem derived from reversible Markov processes. Analytic and computational examples of the methods are used to illustrate the technique, and are shown to reveal the probability transport between almost invariant sets in nonlinear stochastic systems. Both small and large noise cases are considered.
authors.affiliation: Department of Mathematical Sciences, Montclair State University, Montclair, New Jersey 07043, USA.
authors.forename: Lora
authors.initials: L
authors.lastname: Billings
authors.forename: Ira B
authors.initials: IB
authors.lastname: Schwartz
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004196:Disease Outbreaks; D004813:Epidemiology; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011336:Probability; D013269:Stochastic Processes