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dc.contributor.authorBayer, Christian
dc.date.accessioned2017-06-08T06:32:29Z
dc.date.available2017-06-08T06:32:29Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624847
dc.description.abstractA simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/09d290a385244a36b83fa885dbd60b301d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleSDE based regression for random PDEs
dc.typePresentation
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionWeierstrass Institute
refterms.dateFOA2018-06-14T03:40:52Z


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