Stochastic least-squares petrov-galerkin method for parameterized linear system

Kookjin Lee, Kevin Carlberg, Howard C. Elman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We consider the numerical solution of parameterized linear systems where the system matrix, the solution, and the right-hand side are parameterized by a set of uncertain input parameters. We explore spectral methods in which the solutions are approximated in a chosen finite-dimensional subspace. It has been shown that the stochastic Galerkin projection technique fails to minimize any measure of the solution error [A. Mugler and H.-J. Starkloff, ESAIM Math. Model. Numer. Anal., 47 (2013), pp. 1237-1263]. As a remedy for this, we propose a novel stochatic least-squares Petrov-Galerkin (LSPG) method. The proposed method is optimal in the sense that it produces the solution that minimizes a weighted2-norm of the residual over all solutions in a given finite-dimensional subspace. Moreover, the method can be adapted to minimize the solution error in different weighted2-norms by simply applying a weighting function within the least-squares formulation. In addition, a goal-oriented seminorm induced by an output quantity of interest can be minimized by defining a weighting function as a linear functional of the solution. We establish optimality and error bounds for the proposed method, and extensive numerical experiments show that the weighted LSPG method outperforms other spectral methods in minimizing corresponding target weighted norms.

Original languageEnglish (US)
Pages (from-to)374-396
Number of pages23
JournalSIAM-ASA Journal on Uncertainty Quantification
Volume6
Issue number1
DOIs
StatePublished - 2018
Externally publishedYes

Keywords

  • Least-squares Petrov-Galerkin projection
  • Residual minimization
  • Spectral projection
  • Stochastic Galerkin

ASJC Scopus subject areas

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

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