Convergence and rate optimality of adaptive multilevel stochastic Galerkin FEM

Tools

Bespalov, Alex and Praetorius, Dirk and Ruggeri, Michele (2022) Convergence and rate optimality of adaptive multilevel stochastic Galerkin FEM. IMA Journal of Numerical Analysis, 42 (3). 2190–2213. ISSN 0272-4979 (https://doi.org/10.1093/imanum/drab036)

[thumbnail of Bespalov-etal-IMAJNA-2021-Convergence-and-rate-optimality-of-adaptive-multilevel-stochastic-Galerkin-FEM]
Preview
 Text. Filename: Bespalov_etal_IMAJNA_2021_Convergence_and_rate_optimality_of_adaptive_multilevel_stochastic_Galerkin_FEM.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (967kB)| Preview

Abstract

We analyze an adaptive algorithm for the numerical solution of parametric elliptic partial differential equations in two-dimensional physical domains, with coefficients and right-hand-side functions depending on infinitely many (stochastic) parameters. The algorithm generates multilevel stochastic Galerkin approximations; these are represented in terms of a sparse generalized polynomial chaos expansion with coefficients residing in finite element spaces associated with different locally refined meshes. Adaptivity is driven by a two-level a posteriori error estimator and employs a Dörfler-type marking on the joint set of spatial and parametric error indicators. We show that, under an appropriate saturation assumption, the proposed adaptive strategy yields optimal convergence rates with respect to the overall dimension of the underlying multilevel approximation spaces.

ORCID iDs

Bespalov, Alex, Praetorius, Dirk and Ruggeri, Michele ORCID logoORCID: https://orcid.org/0000-0001-6213-1602;
CORE (COnnecting REpositories)