Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients
In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number $N$ of random inputs and an analytic dependence of the solution of the PDE with respect to the parameters in a polydisc of the complex plane $C^N$. We show that a quasi-optimal approximation is given by a Galerkin projection on a weighted (anisotropic) total degree space and prove a (sub)exponential convergence rate. As a specific application we consider a thermal conduction problem with non-overlapping inclusions of random conductivity. Numerical results show the sharpness of our estimates.
Keywords: Uncertainty Quantification ; Elliptic PDEs with random data ; Multivariate polynomial approximation ; Best M –terms polynomial approximation ; Stochastic Galerkin method ; Sub-exponential convergence
Record created on 2012-08-22, modified on 2016-08-09