Sparse finite element methods for operator equations with stochastic data

Abstract

Let A: V → V′ be a strongly elliptic operator on a d-dimensional manifold D (polyhedra or boundaries of polyhedra are also allowed). An operator equation Au = f with stochastic data f is considered. The goal of the computation is the mean field and higher moments $$\mathcal{M}^1 u \in V,\mathcal{M}^2 u \in V \otimes V,...,\mathcal{M}^k u \in V \otimes ... \otimes V$$ of the solution.We discretize the mean field problem using a FEM with hierarchical basis and N degrees of freedom. We present a Monte-Carlo algorithm and a deterministic algorithm for the approximation of the moment $$\mathcal{M}^k u$$ for k⩾1.The key tool in both algorithms is a “sparse tensor product” space for the approximation of $$\mathcal{M}^k u$$ with O(N(log N) k−1) degrees of freedom, instead of N k degrees of freedom for the full tensor product FEM space.A sparse Monte-Carlo FEM with M samples (i.e., deterministic solver) is proved to yield approximations to $$\mathcal{M}^k u$$ with a work of O(M N(log N) k−1) operations. The solutions are shown to converge with the optimal rates with respect to the Finite Element degrees of freedom N and the number M of samples.The deterministic FEM is based on deterministic equations for $$\mathcal{M}^k u$$ in D k ⊂ ℝkd. Their Galerkin approximation using sparse tensor products of the FE spaces in D allows approximation of $$\mathcal{M}^k u$$ with O(N(log N) k−1) degrees of freedom converging at an optimal rate (up to logs).For nonlocal operators wavelet compression of the operators is used. The linear systems are solved iteratively with multilevel preconditioning. This yields an approximation for $$\mathcal{M}^k u$$ with at most O(N (log N) k+1) operations.

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