Multi-element probabilistic collocation method in high dimensions

Jasmine Foo, George Em Karniadakis

Research output: Contribution to journalArticlepeer-review

163 Scopus citations

Abstract

We combine multi-element polynomial chaos with analysis of variance (ANOVA) functional decomposition to enhance the convergence rate of polynomial chaos in high dimensions and in problems with low stochastic regularity. Specifically, we employ the multi-element probabilistic collocation method MEPCM [1] and so we refer to the new method as MEPCM-A. We investigate the dependence of the convergence of MEPCM-A on two decomposition parameters, the polynomial order μ and the effective dimension ν, with ν ≪ N, and N the nominal dimension. Numerical tests for multi-dimensional integration and for stochastic elliptic problems suggest that ν ≥ μ for monotonic convergence of the method. We also employ MEPCM-A to obtain error bars for the piezometric head at the Hanford nuclear waste site under stochastic hydraulic conductivity conditions. Finally, we compare the cost of MEPCM-A against Monte Carlo in several hundred dimensions, and we find MEPCM-A to be more efficient for up to 600 dimensions for a specific multi-dimensional integration problem involving a discontinuous function.

Original languageEnglish (US)
Pages (from-to)1536-1557
Number of pages22
JournalJournal of Computational Physics
Volume229
Issue number5
DOIs
StatePublished - Mar 1 2010

Keywords

  • Domain decomposition
  • Sparse grids
  • Stochastic partial differential equations

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