A priori error analysis of stochastic galerkin mixed approximations of elliptic pdes with random data

We construct stochastic Galerkin approximations to the solution of a first-order system of PDEs with random coefficients. Under the standard finite-dimensional noise assumption, we transform the variational saddle point problem to a parametric deterministic one. Approximations are constructed by combining mixed finite elements on the computational domain with M-variate tensor product polynomials. We study the inf-sup stability and well-posedness of the continuous and finite-dimensional problems, the regularity of solutions with respect to the M parameters describing the random coefficients, and establish a priori error estimates for stochastic Galerkin finite element approximations.