An adaptive superconvergent finite element method based on local residual minimization

Fuente: arXiv
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Autori principali: Muga, Ignacio, Rojas, Sergio, Vega, Patrick
Natura: Preprint
Pubblicazione: 2022
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author Muga, Ignacio
Rojas, Sergio
Vega, Patrick
author_facet Muga, Ignacio
Rojas, Sergio
Vega, Patrick
contents We introduce an adaptive superconvergent finite element method for a class of mixed formulations to solve partial differential equations involving a diffusion term. It combines a superconvergent postprocessing technique for the primal variable with an adaptive finite element method via residual minimization. Such a residual minimization procedure is performed on a local postprocessing scheme, commonly used in the context of mixed finite element methods. Given the local nature of that approach, the underlying saddle point problems associated with residual minimizations can be solved with minimal computational effort. We propose and study a posteriori error estimators, including the built-in residual representative associated with residual minimization schemes; and an improved estimator which adds, on the one hand, a residual term quantifying the mismatch between discrete fluxes and, on the other hand, the interelement jumps of the postprocessed solution. We present numerical experiments in two dimensions using Brezzi-Douglas-Marini elements as input for our methodology. The experiments perfectly fit our key theoretical findings and suggest that our estimates are sharp.
format Preprint
id arxiv_https___arxiv_org_abs_2210_00390
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle An adaptive superconvergent finite element method based on local residual minimization
Muga, Ignacio
Rojas, Sergio
Vega, Patrick
Numerical Analysis
65N12, 65N15, 65N22, 65N30, 65N50
We introduce an adaptive superconvergent finite element method for a class of mixed formulations to solve partial differential equations involving a diffusion term. It combines a superconvergent postprocessing technique for the primal variable with an adaptive finite element method via residual minimization. Such a residual minimization procedure is performed on a local postprocessing scheme, commonly used in the context of mixed finite element methods. Given the local nature of that approach, the underlying saddle point problems associated with residual minimizations can be solved with minimal computational effort. We propose and study a posteriori error estimators, including the built-in residual representative associated with residual minimization schemes; and an improved estimator which adds, on the one hand, a residual term quantifying the mismatch between discrete fluxes and, on the other hand, the interelement jumps of the postprocessed solution. We present numerical experiments in two dimensions using Brezzi-Douglas-Marini elements as input for our methodology. The experiments perfectly fit our key theoretical findings and suggest that our estimates are sharp.
title An adaptive superconvergent finite element method based on local residual minimization
topic Numerical Analysis
65N12, 65N15, 65N22, 65N30, 65N50
url https://arxiv.org/abs/2210.00390