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      Stochastic Galerkin reduced basis methods for parametrized random elliptic PDEs

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            Abstract

            We consider the estimation of parameter-dependent statistical outputs for parametrized elliptic PDE problems with random data. We propose a stochastic Galerkin reduced basis method, which provides the expected output for a given parameter value at the cost of solving a single low-dimensional system of equations. This is substantially faster than usual Monte Carlo reduced basis methods, which require multiple samples of the reduced solution.

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            Author and article information

            Journal
            ScienceOpen Posters
            ScienceOpen
            27 April 2018
            Affiliations
            [1 ]Technische Universität Darmstadt, Graduate School of Computational Engineering
            [2 ]Technische Universität Darmstadt, Department of Mathematics
            [* ]Correspondence: ullmann@ 123456gsc.tu-darmstadt.de
            Article
            10.14293/P2199-8442.1.SOP-MATH.FFVLPM.v1
            47586c39-45b8-42b6-a1c9-69a9f4e3c935
            Copyright © 2018

            This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

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