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Conference Paper: A Dynamically Bi-Orthogonal Method for Time-Dependent Stochastic Partial Differential Equation

TitleA Dynamically Bi-Orthogonal Method for Time-Dependent Stochastic Partial Differential Equation
Authors
Zhang, Z
Issue Date2016
PublisherSociety for Industrial and Applied Mathematics.
Citation
SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, 5-8 April 2016 How to Cite?
AbstractWe propose a dynamically bi-orthogonal method (DyBO) to study time dependent stochastic partial differential equations (SPDEs). The objective of our method is to exploit some intrinsic sparse structure in the stochastic solution by constructing the sparsest representation of the stochastic solution via a bi-orthogonal basis. In this talk, we derive an equivalent system that governs the evolution of the spatial and stochastic basis in the KL expansion. Several numerical experiments will be provided to demonstrate the effectiveness of the DyBO method.
DescriptionSession MS3: Uncertainty Quantification for Hyperbolic and Kinetic Equations - Part I of II
Persistent Identifierhttp://hdl.handle.net/10722/236555

 

DC FieldValueLanguage
dc.contributor.authorZhang, Z-
dc.date.accessioned2016-11-25T10:19:05Z-
dc.date.available2016-11-25T10:19:05Z-
dc.date.issued2016-
dc.identifier.citationSIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, 5-8 April 2016-
dc.identifier.urihttp://hdl.handle.net/10722/236555-
dc.descriptionSession MS3: Uncertainty Quantification for Hyperbolic and Kinetic Equations - Part I of II-
dc.description.abstractWe propose a dynamically bi-orthogonal method (DyBO) to study time dependent stochastic partial differential equations (SPDEs). The objective of our method is to exploit some intrinsic sparse structure in the stochastic solution by constructing the sparsest representation of the stochastic solution via a bi-orthogonal basis. In this talk, we derive an equivalent system that governs the evolution of the spatial and stochastic basis in the KL expansion. Several numerical experiments will be provided to demonstrate the effectiveness of the DyBO method.-
dc.languageeng-
dc.publisherSociety for Industrial and Applied Mathematics. -
dc.relation.ispartofSIAM Conference on Uncertainty Quantification, 2016-
dc.titleA Dynamically Bi-Orthogonal Method for Time-Dependent Stochastic Partial Differential Equation-
dc.typeConference_Paper-
dc.identifier.emailZhang, Z: zhangzw@hku.hk-
dc.identifier.authorityZhang, Z=rp02087-
dc.identifier.hkuros270006-

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