The multigrid discretization of mixed discontinuous Galerkin method for the biharmonic eigenvalue problem

The Ciarlet–Raviart mixed method is popular for the biharmonic equations/eigenvalue problem. In this paper, we propose a multigrid discretization based on the shifted‐inverse iteration of Ciarlet–Raviart mixed discontinuous Galerkin method for the biharmonic eigenvalue problem. We prove the a priori error estimates of the approximate eigenpairs. We also give the a posteriori error estimates of the approximate eigenvalues and prove the reliability of the estimator and implement adaptive computation. Numerical experiments show that our method can efficiently compute biharmonic eigenvalues.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2024-09-05
DOI
https://doi.org/10.1002/mma.10455
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.00

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article

The multigrid discretization of mixed discontinuous Galerkin method for the biharmonic eigenvalue problem

Yidu Yang, Shixi Wang, Jinhua Feng, Hai Bi
Mathematical Methods in the Applied Sciences
Advanced Numerical Methods in Computational Mathematics
article

The multigrid discretization of mixed discontinuous Galerkin method for the biharmonic eigenvalue problem

Yidu Yang, Shixi Wang, Jinhua Feng, Hai Bi
article en

Abstract

The Ciarlet–Raviart mixed method is popular for the biharmonic equations/eigenvalue problem. In this paper, we propose a multigrid discretization based on the shifted‐inverse iteration of Ciarlet–Raviart mixed discontinuous Galerkin method for the biharmonic eigenvalue problem. We prove the a priori error estimates of the approximate eigenpairs. We also give the a posteriori error estimates of the approximate eigenvalues and prove the reliability of the estimator and implement adaptive computation. Numerical experiments show that our method can efficiently compute biharmonic eigenvalues.

Mathematical Methods in the Applied SciencesVol. 48(2)
Guizhou Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Advanced Numerical Methods in Computational Mathematics
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