Convergence analysis of the adaptive stochastic collocation finite element method

This paper is focused on the convergence analysis of an adaptive stochastic collocation algorithm for the stationary diffusion equation with parametric coefficient. The algorithm employs sparse grid collocation in the parameter domain alongside finite element approximations in the spatial domain, and adaptivity is driven by recently proposed parametric and spatial a posteriori error indicators. We prove that for a general diffusion coefficient with finite-dimensional parametrization, the algorithm drives the underlying error estimates to zero. Thus, our analysis covers problems with affine and nonaffine parametric coefficient dependence.

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Publication Details

Journal
Computers & Mathematics with Applications
Published
2026-10-08
DOI
https://doi.org/10.1016/j.camwa.2026.09.045
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
0.00

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article

Convergence analysis of the adaptive stochastic collocation finite element method

Andrey A. Savinov, Alex Bespalov
Computers & Mathematics with Applications
Probabilistic and Robust Engineering Design
article

Convergence analysis of the adaptive stochastic collocation finite element method

Andrey A. Savinov, Alex Bespalov
article en

Abstract

This paper is focused on the convergence analysis of an adaptive stochastic collocation algorithm for the stationary diffusion equation with parametric coefficient. The algorithm employs sparse grid collocation in the parameter domain alongside finite element approximations in the spatial domain, and adaptivity is driven by recently proposed parametric and spatial a posteriori error indicators. We prove that for a general diffusion coefficient with finite-dimensional parametrization, the algorithm drives the underlying error estimates to zero. Thus, our analysis covers problems with affine and nonaffine parametric coefficient dependence.

Computers & Mathematics with ApplicationsVol. 222
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 100%
Probabilistic and Robust Engineering Design
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