A hybrid collocation-perturbation approach for PDEs with random domains

Julio E. Castrillón-Candás, Fabio Nobile, Raul Tempone

Research output: Contribution to journalArticlepeer-review

Abstract

Consider a linear elliptic PDE defined over a stochastic stochastic geometry a function of N random variables. In many application, quantify the uncertainty propagated to a quantity of interest (QoI) is an important problem. The random domain is split into large and small variations contributions. The large variations are approximated by applying a sparse grid stochastic collocation method. The small variations are approximated with a stochastic collocation-perturbation method and added as a correction term to the large variation sparse grid component. Convergence rates for the variance of the QoI are derived and compared to those obtained in numerical experiments. Our approach significantly reduces the dimensionality of the stochastic problem making it suitable for large dimensional problems. The computational cost of the correction term increases at most quadratically with respect to the number of dimensions of the small variations. Moreover, for the case that the small and large variations are independent the cost increases linearly.
Original languageEnglish (US)
JournalAdvances in Computational Mathematics
Volume47
Issue number3
DOIs
StatePublished - May 2 2021

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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