Nobile, F., Tempone, R., and Webster, C.G.. "An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data" SIAM J. Numer. Anal.. 46
(5).
2008.
pp. 2411--2442.
This work proposes and analyzes an anisotropic sparse grid stochastic collocation method for solving partial differential equations with random coefficients and forcing terms (input data of the model). The method consists of a Galerkin approximation in the space variables and a collocation, in probability space, on sparse tensor product grids utilizing either ClenshawâCurtis or Gaussian knots. Even in the presence of nonlinearities, the collocation approach leads to the solution of uncoupled deterministic problems, just as in the Monte Carlo method. This work includes a priori and a posteriori procedures to adapt the anisotropy of the sparse grids to each given problem. These procedures seem to be very effective for the problems under study. The proposed method combines the advantages of isotropic sparse collocation with those of anisotropic full tensor product collocation: the first approach is effective for problems depending on random variables which weigh approximately equally in the solution, while the benefits of the latter approach become apparent when solving highly anisotropic problems depending on a relatively small number of random variables, as in the case where input random variables are KarhunenâLo`ve truncations of âsmoothâ random fields. This work also provides a rigorous convergence analysis of the fully discrete problem and demonstrates (sub)exponential convergence in the asymptotic regime and algebraic convergence in the preasymptotic regime, with respect to the total number of collocation points. It also shows that the anisotropic approximation breaks the curse of dimensionality for a wide set of problems. Numerical examples illustrate the theoretical results and are used to compare this approach with several others, including the standard Monte Carlo. In particular, for moderately large-dimensional problems, the sparse grid approach with a properly chosen anisotropy seems to be very efficient and superior to all examined methods.
Keywords: collocation techniques ; PDEs with random data ; differential equations ; finite elements ; uncertainty quantification ; anisotropic sparse grids ; Smolyak sparse approximation ; multivariate polynomial approximation
Nobile, F., Tempone, R., and Webster, C.G.. "A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data" SIAM J. Numer. Anal.. 46
(5).
2008.
pp. 2309--2345.
In this paper we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables. The method consists in a Galerkin approximation in space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo approach. It can be seen as a generalization of the stochastic Galerkin method proposed in [I. BabuËka, R. Tempone, and G. E. Zouraris, J. Numer. Anal., 42 (2004), pp. 800â825] and allows one to treat easily a wider range of situations, such as input data that depend nonlinearly on the random variables, diffusivity coefficients with unbounded second moments, and random variables that are correlated or even unbounded. We provide a rigorous convergence analysis and demonstrate exponential convergence of the âprobability errorâ with respect to the number of Gauss points in each direction in the probability space, under some regularity assumptions on the random input data. Numerical examples show the effectiveness of the method.
Keywords: collocation techniques ; stochastic PDEs ; finite elements ; uncertainty quantification ; sparse grids ; Smolyak approximation ; multivariate polynomial approximation