A sparse grid stochastic collocation method for elliptic partial differential equations with random input data

This work proposes and analyzes a sparse grid stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms (input data of the model). This method can be viewed as an extension of the Stochastic Collocation method proposed in [Babuška-Nobile-Tempone, Technical report, MOX, Dipartimento di Matematica, 2005] which consists of a Galerkin approximation in space and a collocation at the zeros of suitable tensor product orthogonal polynomials in probability space and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo method. The full tensor product spaces suffer from the curse of dimensionality since the dimension of the approximating space grows exponentially fast in the number of random variables. If the number of random variables is moderately large, this work proposes the use of sparse tensor product spaces utilizing either Clenshaw-Curtis or Gaussian interpolants. For both situations this work provides rigorous convergence analysis of the fully discrete problem and demonstrates: (sub)-exponential convergence of the “probability error” in the asymptotic regime and algebraic convergence of the “probability error” in the pre-asymptotic regime, with respect to the total number of collocation points. The problem setting in which this procedure is recommended as well as suggestions for future enhancements to the method are discussed. Numerical examples exemplify the theoretical results and show the effectiveness of the method.