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Goal-Oriented Adaptivity for Multilevel Stochastic Galerkin FEM with Nonlinear Goal Functionals.

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Title: Goal-Oriented Adaptivity for Multilevel Stochastic Galerkin FEM with Nonlinear Goal Functionals.
Authors: Bespalov, Alex1 (AUTHOR) a.bespalov@bham.ac.uk, Praetorius, Dirk2 (AUTHOR) dirk.praetorius@asc.tuwien.ac.at, Ruggeri, Michele3 (AUTHOR) m.ruggeri@unibo.it
Source: SIAM Journal on Scientific Computing. 2026, Vol. 48 Issue 1, pA392-A417. 26p.
Subjects: Finite element method, Elliptic differential equations, Nonlinear functional analysis, Numerical calculations, Numerical analysis, Numerical solutions to partial differential equations, Error analysis in mathematics
Abstract: This paper is concerned with the numerical approximation of quantities of interest associated with solutions to parametric elliptic PDEs. The key novelty of this work is in its focus on the quantities of interest represented by continuously Gâteaux differentiable nonlinear functionals. We consider a class of parametric elliptic PDEs where the underlying differential operator has affine dependence on a countably infinite number of uncertain parameters. We design a goal-oriented adaptive algorithm for approximating nonlinear functionals of solutions to this class of parametric PDEs. In the algorithm, the approximations of parametric solutions to the primal and dual problems are computed using the multilevel stochastic Galerkin finite element method (SGFEM), and the adaptive refinement process is guided by reliable spatial and parametric error reduction indicators. We prove that the proposed algorithm generates multilevel SGFEM approximations for which the estimates of the error in the goal functional converge to zero. Numerical experiments for a selection of test problems and nonlinear quantities of interest illustrate and underpin our theoretical findings. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:This paper is concerned with the numerical approximation of quantities of interest associated with solutions to parametric elliptic PDEs. The key novelty of this work is in its focus on the quantities of interest represented by continuously Gâteaux differentiable nonlinear functionals. We consider a class of parametric elliptic PDEs where the underlying differential operator has affine dependence on a countably infinite number of uncertain parameters. We design a goal-oriented adaptive algorithm for approximating nonlinear functionals of solutions to this class of parametric PDEs. In the algorithm, the approximations of parametric solutions to the primal and dual problems are computed using the multilevel stochastic Galerkin finite element method (SGFEM), and the adaptive refinement process is guided by reliable spatial and parametric error reduction indicators. We prove that the proposed algorithm generates multilevel SGFEM approximations for which the estimates of the error in the goal functional converge to zero. Numerical experiments for a selection of test problems and nonlinear quantities of interest illustrate and underpin our theoretical findings. [ABSTRACT FROM AUTHOR]
ISSN:10648275
DOI:10.1137/23M1597678