Efficient adaptive stochastic Galerkin methods for parametric operator equations

Alex Bespalov, David Silvester

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    This paper is concerned with the design and implementation of efficient solution algorithms for elliptic PDE problems with correlated random data. The energy orthogonality that is built into stochastic Galerkin approximations is cleverly exploited to give an innovative energy error estimation strategy that utilizes the tensor product structure of the approximation space. An associated error estimator is constructed and shown theoretically and numerically to be an effective mechanism for driving an adaptive refinement process. The codes used in the numerical studies are available online.
    Original languageEnglish
    Pages (from-to)A2118-A2140
    Number of pages23
    JournalSIAM Journal on Scientific Computing
    Issue number4
    Publication statusPublished - 7 Jul 2016


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