Residual-based a posteriori error estimation for a stochastic magnetostatic problem

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Mac, Duc Hung | Tang, Zuqi | Clenet, Stephane | Creusé, Emmanuel

Edité par HAL CCSD ; Elsevier

International audience. In this paper, we propose an a posteriori error estimator for the numerical approximation of a stochastic magnetostatic problem, whose solution depends on the spatial variable but also on a stochastic one. The spatial discretization is performed with finite elements and the stochastic one with a polynomial chaos expansion. As a consequence, the numerical error results from these two levels of discretization. In this paper, we propose an error estimator that takes into account these two sources of error, and which is evaluated from the residuals.

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