Adaptive multi-element polynomial chaos with discrete measure: Algorithms and application to SPDEs

We develop a multi-element probabilistic collocation method (ME-PCM) for arbitrary discrete probability measures with finite moments and apply it to solve partial differential equations with random parameters. The method is based on numerical construction of orthogonal polynomial bases in terms of a...

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Published inApplied numerical mathematics Vol. 90; pp. 91 - 110
Main Authors Zheng, Mengdi, Wan, Xiaoliang, Karniadakis, George Em
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2015
Subjects
Online AccessGet full text
ISSN0168-9274
1873-5460
DOI10.1016/j.apnum.2014.11.006

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Abstract We develop a multi-element probabilistic collocation method (ME-PCM) for arbitrary discrete probability measures with finite moments and apply it to solve partial differential equations with random parameters. The method is based on numerical construction of orthogonal polynomial bases in terms of a discrete probability measure. To this end, we compare the accuracy and efficiency of five different constructions. We develop an adaptive procedure for decomposition of the parametric space using the local variance criterion. We then couple the ME-PCM with sparse grids to study the Korteweg–de Vries (KdV) equation subject to random excitation, where the random parameters are associated with either a discrete or a continuous probability measure. Numerical experiments demonstrate that the proposed algorithms lead to high accuracy and efficiency for hybrid (discrete–continuous) random inputs.
AbstractList We develop a multi-element probabilistic collocation method (ME-PCM) for arbitrary discrete probability measures with finite moments and apply it to solve partial differential equations with random parameters. The method is based on numerical construction of orthogonal polynomial bases in terms of a discrete probability measure. To this end, we compare the accuracy and efficiency of five different constructions. We develop an adaptive procedure for decomposition of the parametric space using the local variance criterion. We then couple the ME-PCM with sparse grids to study the Korteweg–de Vries (KdV) equation subject to random excitation, where the random parameters are associated with either a discrete or a continuous probability measure. Numerical experiments demonstrate that the proposed algorithms lead to high accuracy and efficiency for hybrid (discrete–continuous) random inputs.
Author Zheng, Mengdi
Karniadakis, George Em
Wan, Xiaoliang
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Keywords KdV equation
Discrete–continuous inputs
Stochastic collocation
Data-driven model
Charlier polynomials
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Snippet We develop a multi-element probabilistic collocation method (ME-PCM) for arbitrary discrete probability measures with finite moments and apply it to solve...
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StartPage 91
SubjectTerms Accuracy
Adaptive algorithms
Algorithms
Charlier polynomials
Construction
Criteria
Data-driven model
Discrete–continuous inputs
KdV equation
Mathematical analysis
Mathematical models
Polynomials
Stochastic collocation
Title Adaptive multi-element polynomial chaos with discrete measure: Algorithms and application to SPDEs
URI https://dx.doi.org/10.1016/j.apnum.2014.11.006
https://www.proquest.com/docview/1669874140
Volume 90
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