The weak Galerkin finite element method with or without stabilizer for the damped sine-Gordon equation and its reduced-order simulation

In this work, an efficient scheme is proposed for the 2D damped sine-Gordon equation. The new scheme utilizes the unified weak Galerkin finite element method with or without stabilizer (UWG) in the spatial direction and the second-order explicit differentiation formula in the temporal direction. Both semi discrete schemes and fully discrete schemes are analyzed. First, we demonstrate the stability of the schemes. Subsequently, optimal error estimates are derived in the energy norm and the $ L^2 $ norm. Furthermore, by combining the proper orthogonal decomposition (POD) technique with the WG method, we develop a reduced-order algorithm, significantly improving computational efficiency. Finally, the theoretical results are validated by numerical experiments.

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Publication Details

Journal
Electronic Research Archive
Published
2025-01-01
DOI
https://doi.org/10.3934/era.2025242
Primary Topic
Model Reduction and Neural Networks
Type
article
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The weak Galerkin finite element method with or without stabilizer for the damped sine-Gordon equation and its reduced-order simulation

Senwen Deng, Li Zhang, Minfu Feng, Zhiheng Li
Electronic Research Archive
Model Reduction and Neural Networks
article

The weak Galerkin finite element method with or without stabilizer for the damped sine-Gordon equation and its reduced-order simulation

Senwen Deng, Li Zhang, Minfu Feng, Zhiheng Li
article en

Abstract

In this work, an efficient scheme is proposed for the 2D damped sine-Gordon equation. The new scheme utilizes the unified weak Galerkin finite element method with or without stabilizer (UWG) in the spatial direction and the second-order explicit differentiation formula in the temporal direction. Both semi discrete schemes and fully discrete schemes are analyzed. First, we demonstrate the stability of the schemes. Subsequently, optimal error estimates are derived in the energy norm and the $ L^2 $ norm. Furthermore, by combining the proper orthogonal decomposition (POD) technique with the WG method, we develop a reduced-order algorithm, significantly improving computational efficiency. Finally, the theoretical results are validated by numerical experiments.

Electronic Research ArchiveVol. 33(9)
Sichuan University (CN), Chengdu University of Technology (CN), Chengdu University (CN), Sichuan Normal University (CN)
Openalex Percentile: Top 81%
Model Reduction and Neural Networks
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