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Error analysis of virtual element method for the Poisson–Boltzmann equation

  • Linghan Huang , Shi Shu und Ying Yang EMAIL logo
Veröffentlicht/Copyright: 24. Februar 2025

Abstract

The Poisson–Boltzmann equation, which incorporates the source of the Dirac distribution, has been widely applied in predicting the electrostatic potential of biomolecular systems in solution. In this paper we discuss and analyse the virtual element method for the Poisson–Boltzmann equation on general polyhedral meshes. Nearly optimal error estimates, approaching the best possible accuracy, are achieved for the virtual element approximation in both the L 2-norm and H 1-norm, even when the solution of the entire domain has low regularity. The efficiency of the virtual element method and the validity of the proposed theoretical prediction are confirmed through numerical experiments conducted on various polyhedral meshes.

2010 MSC: 65N30; 65N12

Corresponding author: Ying Yang, School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guangxi Applied Mathematics Center (GUET), Guilin, 541004, Guangxi, P.R. China, E-mail: 

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 12161026

Acknowledgments

The authors thank Jianhua Chen and Yang Liu for their valuable discussions on numerical experiments.

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: In writing, we use ChatGPT to correct grammar and spelling mistakes.

  5. Competing interests: The authors state no conflict of interest.

  6. Research funding: Y. Yang was supported by the National Natural Science Foundation of China (Grant No. 12161026), Guangxi Natural Science Foundation (2020GXNSFAA159098). S. Shu was supported by the National Natural Science Foundation of China (Grant No. 12371373) and Science Challenge Project (Grant No. TZ2024009).

  7. Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Received: 2023-07-25
Accepted: 2024-09-30
Published Online: 2025-02-24
Published in Print: 2025-06-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 26.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jnma-2023-0085/html
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