Convergence Rates of Best N-term Galerkin Approximations for a Class of Elliptic sPDEs
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Deterministic Galerkin approximations of a class of second order elliptic PDEs with random coefficients on a bounded domain D d are introduced and their convergence rates are estimated. The approximations are based on expansions of the random diffusion coefficients in L 2 (D)-orthogonal bases, and on viewing the coefficients of these expansions as random parameters y = y() = (y i ()). This yields an equivalent parametric deterministic PDE whose solution u(x,y) is a function of both the space variable xD and the in general countably many parameters y. We establish new regularity theorems describing the smoothness properties of the solution u as a map from yU = (-1,1) to V = H 01 (D). These results lead to analytic estimates on the V norms of the coefficients (which are functions of x) in a so-called "generalized polynomial chaos" (gpc) expansion of u. Convergence estimates of approximations of u by best N-term truncated V valued polynomials in the variable yU are established. These estimates are of the form N -r , where the rate of convergence r depends only on the decay of the random input expansion. It is shown that r exceeds the benchmark rate 1/2 afforded by Monte Carlo simulations with N "samples" (i.e., deterministic solves) under mild smoothness conditions on the random diffusion coefficients. A class of fully discrete approximations is obtained by Galerkin approximation from a hierarchic family {V l } l=0 V of finite element spaces in D of the coefficients in the N-term truncated gpc expansions of u(x,y). In contrast to previous works, the level l of spatial resolution is adapted to the gpc coefficient. New regularity theorems describing the smoothness properties of the solution u as a map from yU = (-1,1) to a smoothness space W V are established leading to analytic estimates on the W norms of the gpc coefficients and on their space discretization error. The space W coincides with H 2 (D) H 01 (D) in the case where D is a smooth or convex domain. Our analysis shows that in realistic settings a convergence rate N dof-s in terms of the total number of degrees of freedom N dof can be obtained. Here the rate s is determined by both the best N-term approximation rate r and the approximation order of the space discretization in D. 2010 SFoCM.