An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data

F. Nobile*, Raul Tempone, C. G. Webster

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

350 Scopus citations

Abstract

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 Clenshaw-Curtis 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 Karhunen-Loève truncations of "smooth" 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.

Original languageEnglish (US)
Pages (from-to)2411-2442
Number of pages32
JournalSIAM Journal on Numerical Analysis
Volume46
Issue number5
DOIs
StatePublished - Nov 10 2008

Keywords

  • Anisotropic sparse grids
  • Collocation techniques
  • Differential equations
  • Finite elements
  • Multivariate polynomial approximation
  • PDEs with random data
  • Smolyak sparse approximation
  • Uncertainty quantification

ASJC Scopus subject areas

  • Numerical Analysis

Fingerprint Dive into the research topics of 'An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data'. Together they form a unique fingerprint.

Cite this