Journal
ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION MATHEMATIQUE ET ANALYSE NUMERIQUE
Volume 49, Issue 5, Pages 1367-1398Publisher
EDP SCIENCES S A
DOI: 10.1051/m2an/2015017
Keywords
Partial differential equations with random coefficients; generalized polynomial chaos; adaptive finite element methods; contraction property; residual a posteriori error estimation; uncertainty quantification
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Funding
- AFOSR
- DOE
- NNSA
- NSF
- European Research Council (ERC) [AdG247277]
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We analyze a posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin Finite Element methods for countably-parametric, elliptic boundary value problems. A residual error estimator which separates the effects of gpc-Galerkin discretization in parameter space and of the Finite Element discretization in physical space in energy norm is established. It is proved that the adaptive algorithm converges. To this end, a contraction property of its iterates is proved. It is shown that the sequences of triangulations which are produced by the algorithm in the FE discretization of the active gpc coefficients are asymptotically optimal. Numerical experiments illustrate the theoretical results.
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