The mixed penalty method for the Signorini problem

We investigate two penalty approaches for the Signorini problem in the mixed form. The well-posedness theory has been established for the two mixed penalty problems in both the continuous and discrete sense. For the continuous case, we obtain the error of the penalty for both two approaches. We also study the error estimates of the discrete mixed penalty problems, which depend on the mesh size and the penalty parameter. Based on the two penalty approaches, we design two algorithms and show their convergence. Several numerical experiments are carried out to confirm the theoretical results.

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Publication Details

Journal
ESAIM. Mathematical modelling and numerical analysis
Published
2024-09-01
DOI
https://doi.org/10.1051/m2an/2024066
Citations
3
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
1.12

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article

The mixed penalty method for the Signorini problem

Qi Wang, Guanyu Zhou, Jingyan Hu
3 citations
ESAIM. Mathematical modelling and numerical analysis
Advanced Numerical Methods in Computational Mathematics
1.12
article

The mixed penalty method for the Signorini problem

Qi Wang, Guanyu Zhou, Jingyan Hu
article en
3 citations

Abstract

We investigate two penalty approaches for the Signorini problem in the mixed form. The well-posedness theory has been established for the two mixed penalty problems in both the continuous and discrete sense. For the continuous case, we obtain the error of the penalty for both two approaches. We also study the error estimates of the discrete mixed penalty problems, which depend on the mesh size and the penalty parameter. Based on the two penalty approaches, we design two algorithms and show their convergence. Several numerical experiments are carried out to confirm the theoretical results.

ESAIM. Mathematical modelling and numerical analysisVol. 58(5)
University of Electronic Science and Technology of China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Advanced Numerical Methods in Computational Mathematics
1.12
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