A stochastic Galerkin method for optimal Dirichlet boundary control problems with uncertain data

The paper deals with a stochastic Galerkin approximation of elliptic Dirichlet boundary control problems with random input data. The expectation of a tracking cost functional with the deterministic constrained control is minimized. Error estimates are derived for the control variable in $L^2(\mathrm{\partial }\mathcal{D})$-norm and state variable in $L^2(\mathrm{\Omega }\times \mathcal{D})$-norm. To solve large linear systems, appropriate preconditioners are proposed for both unconstrained and constrained scenarios. To illustrate the validity and efficiency of the proposed approaches, some numerical experiments are performed.