Work

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

Year: 2026

Type: article

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.

Source: SIAM Journal on Scientific Computing

DOI: 10.1137/23m1597678

Authors: Alex Bespalov, Dirk Praetorius, Michele Ruggeri

Institutions: University of Birmingham, TU Wien, University of Bologna

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green