A finite element method for elliptic problems with stochastic input data
Applied Numerical Mathematics
Fast Matrix-Vector Multiplication in the Sparse-Grid Galerkin Method
Journal of Scientific Computing
On the low-rank approximation by the pivoted Cholesky decomposition
Applied Numerical Mathematics
Combination technique based k-th moment analysis of elliptic problems with random diffusion
Journal of Computational Physics
Journal of Computational and Applied Mathematics
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For Au = f with an elliptic differential operator $${A:\mathcal{H} \rightarrow \mathcal{H}'}$$ and stochastic data f, the m-point correlation function $${{\mathcal M}^m u}$$ of the random solution u satisfies a deterministic equation with the m-fold tensor product operator A (m) of A. Sparse tensor products of hierarchic FE-spaces in $${\mathcal{H}}$$ are known to allow for approximations to $${{\mathcal M}^m u}$$ which converge at essentially the rate as in the case m = 1, i.e. for the deterministic problem. They can be realized by wavelet-type FE bases (von Petersdorff and Schwab in Appl Math 51(2):145–180, 2006; Schwab and Todor in Computing 71:43–63, 2003). If wavelet bases are not available, we show here how to achieve the fast computation of sparse approximations of $${{\mathcal M}^m u}$$ for Galerkin discretizations of A by multilevel frames such as BPX or other multilevel preconditioners of any standard FEM approximation for A. Numerical examples illustrate feasibility and scope of the method.