Stochastic basis adaptation and spatial domain decomposition for partial differential equations with random coefficients

R. Tipireddy, P. Stinis, A. M. Tartakovsky

Research output: Contribution to journalArticlepeer-review


We present a novel uncertainty quantification approach for high-dimensional stochastic partial differential equations that reduces the computational cost of polynomial chaos methods by decomposing the computational domain into nonoverlapping subdomains and adapting the stochastic basis in each subdomain so the local solution has a lower dimensional random space representation. The local solutions are coupled using the Neumann-Neumann algorithm, where we first estimate the interface solution then evaluate the interior solution in each subdomain using the interface solution as a boundary condition. The interior solutions in each subdomain are computed independently of each other, which reduces the operation count from O(Nα) to O(Mα), where N is the total number of degrees of freedom, M is the number of degrees of freedom in each subdomain, and the exponent α > 1 depends on the uncertainty quantification method used. In addition, the localized nature of solutions makes the proposed approach highly parallelizable. We illustrate the accuracy and efficiency of the approach for linear and nonlinear differential equations with random coefficients.

Original languageEnglish (US)
Pages (from-to)273-301
Number of pages29
JournalSIAM-ASA Journal on Uncertainty Quantification
Issue number1
StatePublished - 2018
Externally publishedYes


  • Basis adaptation
  • Dimension reduction
  • Domain decomposition
  • Neumann-Neumann algorithm
  • Polynomial chaos
  • Uncertainty quantification

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics
  • Applied Mathematics


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