Nitsche’s serendipity virtual element method for the eigenvalue problem

In this paper, we study the Nitsche’s extended serendipity virtual element method for the eigenvalue problem in two and three dimensions. We start from the introduction of two- and three-dimensional serendipity virtual element spaces, in which the serendipity technique helps us drop all internal-to-face and internal-to-element degrees of freedom with the suitable projection operators fitting into virtual element spaces. Meanwhile, we give the Nitsche’s extended serendipity virtual element scheme of the eigenvalue problem. At the next stage, we prove the spectral approximation and the optimal error estimates of the proposed numerical method. By using the standard interpolation and polynomial approximation properties, we prove the H 1 -norm error bound of the associated source problem. To consider the L 2 -norm error bound, the Ritz-Volterra projection based on the formulation of Nitsche’s virtual element bilinear form is defined. Then we rigorously analyze its approximation properties. After that, we build the L 2 error estimate of the associated source problem. In the main theorems, we prove the error estimates of eigenfunctions and eigenvalues obtained by the Nitsche’s extended serendipity virtual element method. At the final stage, we extend the Nitsche’s idea to arbitrary curved domains by modifying the virtual element scheme with Taylor expansion terms. Numerical experiments confirm the theoretical results, using the Nitsche’s serendipity virtual element method to solve the Laplacian and Shrödinger eigenvalue problems on plane and curved domains.

Authors

Institutions

Publication Details

Journal
Computer Methods in Applied Mechanics and Engineering
Published
2025-08-28
DOI
https://doi.org/10.1016/j.cma.2025.118336
Citations
1
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
1.33
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nitsche’s serendipity virtual element method for the eigenvalue problem

Bing‐Bing Xu, Jian Meng, Qian Xu, Fang Su
1 citations
Computer Methods in Applied Mechanics and Engineering
Advanced Numerical Methods in Computational Mathematics
1.33
article

Nitsche’s serendipity virtual element method for the eigenvalue problem

Bing‐Bing Xu, Jian Meng, Qian Xu, Fang Su
article en
1 citations

Abstract

In this paper, we study the Nitsche’s extended serendipity virtual element method for the eigenvalue problem in two and three dimensions. We start from the introduction of two- and three-dimensional serendipity virtual element spaces, in which the serendipity technique helps us drop all internal-to-face and internal-to-element degrees of freedom with the suitable projection operators fitting into virtual element spaces. Meanwhile, we give the Nitsche’s extended serendipity virtual element scheme of the eigenvalue problem. At the next stage, we prove the spectral approximation and the optimal error estimates of the proposed numerical method. By using the standard interpolation and polynomial approximation properties, we prove the H 1 -norm error bound of the associated source problem. To consider the L 2 -norm error bound, the Ritz-Volterra projection based on the formulation of Nitsche’s virtual element bilinear form is defined. Then we rigorously analyze its approximation properties. After that, we build the L 2 error estimate of the associated source problem. In the main theorems, we prove the error estimates of eigenfunctions and eigenvalues obtained by the Nitsche’s extended serendipity virtual element method. At the final stage, we extend the Nitsche’s idea to arbitrary curved domains by modifying the virtual element scheme with Taylor expansion terms. Numerical experiments confirm the theoretical results, using the Nitsche’s serendipity virtual element method to solve the Laplacian and Shrödinger eigenvalue problems on plane and curved domains.

Computer Methods in Applied Mechanics and EngineeringVol. 446
Leibniz University Hannover (DE), National University of Defense Technology (CN)
Openalex Percentile: Top 19%
Advanced Numerical Methods in Computational Mathematics
1.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.