A posteriori error estimation for stochastic Galerkin approximations

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• Alex Bespalov (Birmingham)
• Monday 10 March 2014, 14:00-15:00
• Watson LRA.

If you have a question about this talk, please contact Alexandra Tzella.

Stochastic Galerkin finite element method is an increasingly popular approach for the solution of elliptic PDE problems with correlated random data. It combines conventional (h-) finite element approximation on the spatial domain with spectral (p-) approximation on a finite-dimensional manifold in the (stochastic) parameter space. Adaptive techniques are now under development to capture this finite-dimensional manifold and to construct efficient spatial and stochastic approximations.

In this talk, we outline the issues involved in a posteriori error analysis of computed solutions and present a practical a posteriori estimator for the approximation error. We also discuss different strategies for enriching the discrete space and derive computable estimates of the error reduction for the corresponding enhanced approximations. We show numerically that these estimates can be used to choose the enrichment strategy that reduces the error most efficiently, and thus they can guide an adaptive refinement algorithm.

This talk is part of the Applied Mathematics Seminar Series series.

This talk is included in these lists:

• Applied Mathematics Seminar Series
• Watson LRA

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