Minimal residual discretization of a class of fully nonlinear elliptic PDE

Abstract This work introduces finite-element methods for a class of elliptic fully nonlinear partial differential equations. They are based on a minimal residual principle that builds upon the Alexandrov–Bakelman–Pucci estimate. Under rather general structural assumptions on the operator, convergence of $C^{1}$ conforming and discontinuous Galerkin methods is proven in the $L^{^\infty} $ norm. Numerical experiments on the performance of adaptive mesh refinement driven by local information of the residual in two and three space dimensions are provided.

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Publication Details

Journal
IMA Journal of Numerical Analysis
Published
2025-08-28
DOI
https://doi.org/10.1093/imanum/draf075
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.00

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article

Minimal residual discretization of a class of fully nonlinear elliptic PDE

Dietmar Gallistl, Ngoc-Tien Tran
IMA Journal of Numerical Analysis
Advanced Numerical Methods in Computational Mathematics
article

Minimal residual discretization of a class of fully nonlinear elliptic PDE

Dietmar Gallistl, Ngoc-Tien Tran
article en

Abstract

Abstract This work introduces finite-element methods for a class of elliptic fully nonlinear partial differential equations. They are based on a minimal residual principle that builds upon the Alexandrov–Bakelman–Pucci estimate. Under rather general structural assumptions on the operator, convergence of $C^{1}$ conforming and discontinuous Galerkin methods is proven in the $L^{^\infty} $ norm. Numerical experiments on the performance of adaptive mesh refinement driven by local information of the residual in two and three space dimensions are provided.

IMA Journal of Numerical Analysis
University of Augsburg (DE), Schiller International University (FR), Friedrich Schiller University Jena (DE)
Horizon 2020 Framework Programme
Openalex Percentile: Top 76%
Advanced Numerical Methods in Computational Mathematics
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