Predictability in Spatially Extended Systems with Model Uncertainty

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dc.creatorDuan, Jinqiao
dc.date2008-12-18
dc.date2009-03-26
dc.date.accessioned2026-07-07T12:56:16Z
dc.date.available2026-07-07T12:56:16Z
dc.descriptionMacroscopic models for spatially extended systems under random influences are often described by stochastic partial differential equations (SPDEs). Some techniques for understanding solutions of such equations, such as estimating correlations, Liapunov exponents and impact of noises, are discussed. They are relevant for understanding predictability in spatially extended systems with model uncertainty, for example, in physics, geophysics and biological sciences. The presentation is for a wide audience.
dc.descriptionTo appear in the Journal "Engineering Simulation", 2009
dc.identifierhttps://arxiv.org/abs/0812.3679
dc.identifierhttp://arxiv.org/abs/0812.3679
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/224526
dc.subjectDynamical Systems
dc.subjectAnalysis of PDEs
dc.subjectProbability
dc.subject60H15, 35R60, 60H30, 37H10
dc.titlePredictability in Spatially Extended Systems with Model Uncertainty
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