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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.

What is MCQ? The Mathematical Citation Quotient (MCQ) measures journal impact by looking at citations over a five-year period. Subscribers to MathSciNet may click through for more detailed information.

 

Optimal analysis of finite element methods for the stochastic Stokes equations
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by Buyang Li, Shu Ma and Weiwei Sun;
Math. Comp. 94 (2025), 551-583
DOI: https://doi.org/10.1090/mcom/3972
Published electronically: April 29, 2024

Abstract:

Numerical analysis for the stochastic Stokes equations is still challenging even though it has been well done for the corresponding deterministic equations. In particular, the pre-existing error estimates of finite element methods for the stochastic Stokes equations in the $L^\infty (0, T; L^2(\Omega ; L^2))$ norm all suffer from the order reduction with respect to the spatial discretizations. The best convergence result obtained for these fully discrete schemes is only half-order in time and first-order in space, which is not optimal in space in the traditional sense. The objective of this article is to establish strong convergence of $O(\tau ^{1/2}+ h^2)$ in the $L^\infty (0, T; L^2(\Omega ; L^2))$ norm for approximating the velocity, and strong convergence of $O(\tau ^{1/2}+ h)$ in the $L^{\infty }(0, T;L^2(\Omega ;L^2))$ norm for approximating the time integral of pressure, where $\tau$ and $h$ denote the temporal step size and spatial mesh size, respectively. The error estimates are of optimal order for the spatial discretization considered in this article (with MINI element), and consistent with the numerical experiments. The analysis is based on the fully discrete Stokes semigroup technique and the corresponding new estimates.
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Bibliographic Information
  • Buyang Li
  • Affiliation: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
  • MR Author ID: 910552
  • Email: buyang.li@polyu.edu.hk
  • Shu Ma
  • Affiliation: Department of Mathematics, National University of Singapore, Singapore 119076
  • ORCID: 0000-0002-9172-9678
  • Email: shuma@nus.edu.sg
  • Weiwei Sun
  • Affiliation: Research Center for Mathematics, Beijing Normal University, Zhuhai, People’s Republic of China; and Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, People’s Republic of China
  • MR Author ID: 268018
  • Email: maweiw@uic.edu.cn
  • Received by editor(s): July 30, 2022
  • Received by editor(s) in revised form: December 11, 2023
  • Published electronically: April 29, 2024
  • Additional Notes: This work was supported in part by the NSFC key program (project no. 12231003), NSFC general program (project no. 12071020), Guangdong Provincial Key Laboratory IRADS (2022B1212010006, UIC-R0400001-22) and Guangdong Higher Education Upgrading Plan (UIC-R0400024-21), the Research Grants Council of Hong Kong (GRF project no. PolyU15301321), and an internal grant of The Hong Kong Polytechnic University (Project ID: P0038843).
    The third author is the corresponding author.
  • © Copyright 2024 American Mathematical Society
  • Journal: Math. Comp. 94 (2025), 551-583
  • MSC (2020): Primary 65M15
  • DOI: https://doi.org/10.1090/mcom/3972