Semidiscrete Galerkin approximation for a linear stochastic parabolic partial differential equation driven by an additive noise
Journal article, 2004

Abstract We study the semidiscrete Galerkin approximation of a stochastic parabolic partial differential equation forced by an additive space-time noise. The discretization in space is done by a piecewise linear finite element method. The space-time noise is approximated by using the generalized L2 projection operator. Optimal strong convergence error estimates in the L2 and norms with respect to the spatial variable are obtained. The proof is based on appropriate nonsmooth data error estimates for the corresponding deterministic parabolic problem. The error estimates are applicable in the multi-dimensional case.

Author

Yubin Yan

Chalmers, Department of Computational Mathematics

University of Gothenburg

BIT (Copenhagen)

0006-3835 (ISSN) 15729125 (eISSN)

Vol. 44 4 829-847

Subject Categories

Computational Mathematics

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Created

10/6/2017