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Schwarz Preconditioner for the Stochastic Finite Element Method

  • Waad SubberEmail author
  • Sébastien Loisel
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 104)

Abstract

The intrusive polynomial chaos approach for uncertainty quantification in numerous engineering problems constitutes a computationally challenging task. Indeed, Galerkin projection in the spectral stochastic finite element method (SSFEM) leads to a large-scale linear system for the polynomial chaos coefficients of the solution process. The development of robust and efficient solution strategies for the resulting linear system therefore is of paramount importance for the applicability of the SSFEM to practical engineering problems. The solution algorithms should be parallel and scalable in order to exploit the available multiprocessor supercomputers. Therefore, we formulate a two-level Schwarz preconditioner for the polynomial chaos based uncertainty quantification of large-scale computational models.

Keywords

Uncertainty Quantification Joint Probability Density Function Polynomial Chaos Input Uncertainty Coarse Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Mathematical and Computer SciencesHeriot-Watt UniversityEdinburghUK

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