A second-order correction method for loosely coupled discretizations applied to parabolic–parabolic interface problems

Abstract We consider a parabolic–parabolic interface problem and construct a loosely coupled prediction-correction scheme based on the Robin–Robin splitting method analyzed in [J. Numer. Math., 31(1):59–77, 2023]. We show that the errors of the correction step converge at $\mathcal O((\varDelta t)^{2})$, under suitable convergence rate assumptions on the discrete time derivative of the prediction step, where $\varDelta t$ stands for the time-step length. Numerical results are shown to support our analysis and the assumptions.

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Publication Details

Journal
IMA Journal of Numerical Analysis
Published
2024-09-13
DOI
https://doi.org/10.1093/imanum/drae075
Citations
3
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
1.12

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article

A second-order correction method for loosely coupled discretizations applied to parabolic–parabolic interface problems

Erik Burman, Sijing Liu, Johnny Guzmán, R. Durst et al.
3 citations
IMA Journal of Numerical Analysis
Advanced Numerical Methods in Computational Mathematics
1.12
article

A second-order correction method for loosely coupled discretizations applied to parabolic–parabolic interface problems

Erik Burman, Sijing Liu, Johnny Guzmán, R. Durst, Miguel Fernández Pérez
article en
3 citations

Abstract

Abstract We consider a parabolic–parabolic interface problem and construct a loosely coupled prediction-correction scheme based on the Robin–Robin splitting method analyzed in [J. Numer. Math., 31(1):59–77, 2023]. We show that the errors of the correction step converge at $\mathcal O((\varDelta t)^{2})$, under suitable convergence rate assumptions on the discrete time derivative of the prediction step, where $\varDelta t$ stands for the time-step length. Numerical results are shown to support our analysis and the assumptions.

IMA Journal of Numerical AnalysisVol. 45(5)
Centre National de la Recherche Scientifique (FR), University of Pittsburgh (US), Brown University (US), Sorbonne Université (FR), University College London (GB)
National Science Foundation, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 29%
Advanced Numerical Methods in Computational Mathematics
1.12
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