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2013.16: Energy norm a posteriori error estimation for parametric operator equations

2013.16: Alex Bespalov, Catherine E. Powell and David Silvester (2014) Energy norm a posteriori error estimation for parametric operator equations. SIAM Journal on Scientific Computing, 36 (2). A339-A363. ISSN 1095-7197

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DOI: 10.1137/130916849


Stochastic Galerkin approximation is an increasingly popular approach for the solution of elliptic PDE problems with correlated random data. A typical strategy is to combine conventional ($h$-) finite element approximation on the spatial domain with spectral ($p$-) approximation on a finite-dimensional manifold in the (stochastic) parameter domain. The issues involved in a posteriori error analysis of computed solutions are outlined in this paper. A novel energy error estimator that uses a parameter-free part of the underlying differential operator is introduced which effectively exploits the tensor product structure of the approximation space. We prove that our error estimator is reliable and efficient. We also discuss different strategies for enriching the approximation space and prove two-sided estimates of the error reduction for the corresponding enhanced approximations. These give computable estimates of the error reduction that depend only on the problem data and the original approximation.

Item Type:Article
Uncontrolled Keywords:stochastic Galerkin methods, stochastic finite elements, random data, Karhunen-Loeve expansion, parametric operator equations, error estimation, a posteriori error analysis
Subjects:MSC 2000 > 35 Partial differential equations
MSC 2000 > 65 Numerical analysis
MIMS number:2013.16
Deposited By:Alex Bespalov
Deposited On:14 April 2013

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