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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

625 Scopus citations

Abstract

This work proposes and analyzes a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems as in the Monte Carlo method. If the number of random variables needed to describe the input data is moderately large, full tensor product spaces are computationally expensive to use due to the curse of dimensionality. In this case the sparse grid approach is still expected to be competitive with the classical Monte Carlo method. Therefore, it is of major practical relevance to understand in which situations the sparse grid stochastic collocation method is more efficient than Monte Carlo. This work provides error estimates for the fully discrete solution using Lq norms and analyzes the computational efficiency of the proposed method. In particular, it demonstrates algebraic convergence with respect to the total number of collocation points and quantifies the effect of the dimension of the problem (number of input random variables) in the final estimates. The derived estimates are then used to compare the method with Monte Carlo, indicating for which problems the former is more efficient than the latter. Computational evidence complements the present theory and shows the effectiveness of the sparse grid stochastic collocation method compared to full tensor and Monte Carlo approaches.

Original languageEnglish (US)
Pages (from-to)2309-2345
Number of pages37
JournalSIAM Journal on Numerical Analysis
Volume46
Issue number5
DOIs
StatePublished - 2008
Externally publishedYes

Keywords

  • Collocation techniques
  • Finite elements
  • Multivariate polynomial approximation
  • Smolyak approximation
  • Sparse grids
  • Stochastic PDEs
  • Uncertainty quantification

ASJC Scopus subject areas

  • Numerical Analysis

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