Mathematics of Computation

Published by the American Mathematical Society since 1960 (published as Mathematical Tables and other Aids to Computation 1943-1959), Mathematics of Computation is devoted to research articles of the highest quality in computational mathematics.

ISSN 1088-6842 (online) ISSN 0025-5718 (print)

The 2024 MCQ for Mathematics of Computation is 1.78.

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Convergence of a stabilized parametric finite element method of the Barrett–Garcke–Nürnberg type for curve shortening flow
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by Genming Bai and Buyang Li;
Math. Comp. 94 (2025), 2151-2220
DOI: https://doi.org/10.1090/mcom/4019
Published electronically: October 3, 2024

Abstract:

The parametric finite element methods of the Barrett–Garcke–Nürnberg (BGN) type have been successful in preventing mesh distortion/ degeneration in approximating the evolution of surfaces under various geometric flows, including mean curvature flow, Willmore flow, Helfrich flow, surface diffusion, and so on. However, the rigorous justification of convergence of the BGN-type methods and the characeterization of the particle trajectories produced by these methods still remain open since this class of methods was proposed in 2007. The main difficulty lies in the stability of the artificial tangential velocity implicitly determined by the BGN methods. In this paper, we give the first proof of convergence of a stabilized BGN method for curve shortening flow, with optimal-order convergence in $L^2$ norm for finite elements of degree $k \geq 2$ under the stepsize condition $\tau \leq c h^{k+1}$ (for any fixed constant $c$). Moreover, we give the first rigorous characterization of the particle trajectories produced by the BGN-type methods for one-dimensional curves, i.e., we prove that the particle trajectories produced by the stabilized BGN methods converge to the particle trajectories determined by a system of geometric partial differential equations which differs from the standard curve shortening flow by a tangential motion. The characterization of the particle trajectories also rigorously explains, for one-dimensional curves, why the BGN-type methods could maintain the quality of the underlying evolving mesh.
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Bibliographic Information
  • Genming Bai
  • Affiliation: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, People’s Republic of China
  • MR Author ID: 1509490
  • ORCID: 0000-0002-4138-8051
  • Email: genming.bai@connect.polyu.hk
  • Buyang Li
  • Affiliation: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, People’s Republic of China
  • MR Author ID: 910552
  • Email: buyang.li@polyu.edu.hk
  • Received by editor(s): October 30, 2023
  • Received by editor(s) in revised form: August 6, 2024
  • Published electronically: October 3, 2024
  • Additional Notes: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU/RFS2324-5S03, PolyU/GRF15303022) and an internal grant of The Hong Kong Polytechnic University (Project ID: P0051154).
    The second author is the corresponding author
  • © Copyright 2024 American Mathematical Society
  • Journal: Math. Comp. 94 (2025), 2151-2220
  • MSC (2020): Primary 65M12, 65M60, 53E10, 53A04, 35R01, 35R35
  • DOI: https://doi.org/10.1090/mcom/4019