Macroscopic reduction for stochastic reaction-diffusion equations

dc.contributor.authorWang, W.
dc.contributor.authorRoberts, A.
dc.contributor.departmentFaculty of Engineering, Computer & Mathematical Sciences
dc.date.issued2013
dc.description.abstractThe macroscopic behavior of dissipative stochastic partial differential equations usually can be described by a finite dimensional system. This article proves that a macroscopic reduced model may be constructed for stochastic reaction-diffusion equations with cubic nonlinearity by artificial separating the system into two distinct slow-fast time parts. An averaging method and a deviation estimate show that the macroscopic reduced model should be a stochastic ordinary equation which includes the random effect transmitted from the microscopic timescale due to the nonlinear interaction. Numerical simulations of an example stochastic heat equation confirms the predictions of this stochastic modelling theory. This theory empowers us to better model the long time dynamics of complex stochastic systems.
dc.description.statementofresponsibilityW. Wang and A. J. Roberts
dc.identifier.citationIMA Journal of Applied Mathematics, 2013; 78(6):1237-1264
dc.identifier.doi10.1093/imamat/hxs019
dc.identifier.issn0272-4960
dc.identifier.issn1464-3634
dc.identifier.orcidRoberts, A. [0000-0001-8930-1552]
dc.identifier.urihttp://hdl.handle.net/2440/80418
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0774311
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0988738
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0988738
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0774311
dc.rights© The authors 2012.
dc.source.urihttps://doi.org/10.1093/imamat/hxs019
dc.subjectstochastic reaction–diffusion equations
dc.subjectaveraging
dc.subjecttightness
dc.subjectmartingale
dc.titleMacroscopic reduction for stochastic reaction-diffusion equations
dc.typeJournal article
pubs.publication-statusPublished

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