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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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Reducing polynomial degree by one for inner-stage operators affects neither stability type nor accuracy order of the Runge–Kutta discontinuous Galerkin method
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by Zheng Sun;
Math. Comp.
DOI: https://doi.org/10.1090/mcom/4037
Published electronically: October 31, 2024

Abstract:

The Runge–Kutta (RK) discontinuous Galerkin (DG) method is a mainstream numerical algorithm for solving hyperbolic equations. In this paper, we use the linear advection equation in one and two dimensions as a model problem to prove the following results: For an arbitrarily high-order RKDG scheme in Butcher form, as long as we use the $P^k$ approximation in the final stage, even if we drop the $k$th-order polynomial modes and use the $P^{k-1}$ approximation for the DG operators at all inner RK stages, the resulting numerical method still maintains the same type of stability and convergence rate as those of the original RKDG method. Numerical examples are provided to validate the analysis. The numerical method analyzed in this paper is a special case of the Class A RKDG method with stage-dependent polynomial spaces proposed by Chen, Sun, and Xing [arXiv preprint, arXiv:2402.15150, 2024]. Our analysis provides theoretical justifications for employing cost-effective and low-order spatial discretization at specific RK stages for developing more efficient DG schemes without affecting stability type and accuracy order of the original method.
References
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Bibliographic Information
  • Zheng Sun
  • Affiliation: Department of Mathematics, The University of Alabama, Box 870350, Tuscaloosa, Alabama 35487
  • ORCID: 0000-0003-3763-3015
  • Email: zsun30@ua.edu
  • Received by editor(s): April 22, 2024
  • Received by editor(s) in revised form: August 29, 2024
  • Published electronically: October 31, 2024
  • Additional Notes: The work of the author was partially supported by the NSF grant DMS-2208391. This work was also partially supported by the NSF grant DMS-1929284 while the author was in residence at the ICERM in Providence, RI, during the semester program “Numerical PDEs: Analysis, Algorithms, and Data Challenges.”
  • © Copyright 2024 American Mathematical Society
  • Journal: Math. Comp.
  • MSC (2020): Primary 65M12, 65M15
  • DOI: https://doi.org/10.1090/mcom/4037