JOURNAL ARTICLE

A Yee-like finite-element scheme for Maxwell's equations on unstructured grids.

  • Published In: IMA Journal of Numerical Analysis, 2025, v. 45, n. 2. P. 1028 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Radu, Bogdan; Egger, Herbert 3 of 3

Abstract

The article focuses on the development and analysis of a novel finite element scheme for efficiently solving time-dependent Maxwell's equations on unstructured grids, extending the classical Yee scheme to more general settings including inhomogeneous lossy media. The proposed method reduces the degrees of freedom to one per edge for most edges while maintaining a sparse inverse mass matrix, enabling explicit time-stepping without solving linear systems. A rigorous convergence analysis is provided, demonstrating first-order accuracy under assumptions on mesh regularity and material parameter continuity, particularly requiring two degrees of freedom at edges with discontinuous conductivity to preserve convergence rates. Numerical experiments in two and three dimensions validate the theoretical results, showing that the reduced scheme achieves comparable accuracy and computational efficiency to standard finite element methods, with benefits in memory usage and time stepping. The work also discusses the algebraic structure of the scheme, its relation to existing Yee-like methods, and highlights challenges and remedies for extending Yee schemes to unstructured grids with discontinuous parameters.

Additional Information

  • Source:IMA Journal of Numerical Analysis. 2025/03, Vol. 45, Issue 2, p1028
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0272-4979
  • DOI:10.1093/imanum/drae023
  • Accession Number:184408190
  • Copyright Statement:Copyright of IMA Journal of Numerical Analysis is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.