A non-adapted sparse approximation of PDEs with stochastic inputs

Abstract

We propose a method for the approximation of solutions of PDEs with stochastic coefficients based on the direct, i.e., non-adapted, sampling of solutions. This sampling can be done by using any legacy code for the deterministic problem as a black box. The method converges in probability (with probabilistic error bounds) as a consequence of sparsity and a concentration of measure phenomenon on the empirical correlation between samples. We show that the method is well suited for truly high-dimensional problems (with slow decay in the spectrum).

References in corpus (2)

• Sparsity and Incoherence in Compressive Sampling
• Alternating Direction Algorithms for ${\ell }_1$-Problems in Compressive Sensing

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