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Abstract
This study explores the use of r-adaptive mesh refinement strategies for elliptic partial differential equations (PDEs) posed on non-convex domains. We introduce an r-adaptive strategy based on a simplified optimal transport method to create a graded mesh, distributing the interpolation error evenly, considering the solution's local asymptotic behaviour. The grading ensures good mesh compression and regularity, regardless of dimension or location. We showcase our approach by studying discontinuous Galerkin (dG) finite element approximations. We utilise a posteriori error estimates for the dG method on general meshes, showing equidistribution across our graded mesh. Numerical tests on ‘L-shaped’ and ‘crack’ domains confirm that our method achieves optimal convergence rates.
| Original language | English |
|---|---|
| Pages (from-to) | 240-258 |
| Number of pages | 19 |
| Journal | Computers and Mathematics with Applications |
| Volume | 192 |
| Early online date | 28 May 2025 |
| DOIs | |
| Publication status | Published - 15 Aug 2025 |
Data Availability Statement
Data will be made available on request.Funding
CJB is supported by EPSRC grant EP/V026259/1. SA was supported by a PhD studentship through the SAMBa CDT EP/S022945/1. TP is grateful for support from the Leverhulme Trust Grant No. RPG-2021-238 and is partially supported by EPSRC grants EP/W026899/2, EP/X017206/1 and EP/X030067/1.
| Funders | Funder number |
|---|---|
| Engineering and Physical Sciences Research Council | EP/V026259/1, EP/W026899/2, EP/X017206/1 , EP/X030067/1 |
ASJC Scopus subject areas
- Modelling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics
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Dive into the research topics of 'Computationally efficient r−adaptive graded meshes over non-convex domains'. Together they form a unique fingerprint.Projects
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Programme Grant: Mathematics of Deep Learning
Budd, C. (PI) & Ehrhardt, M. (CoI)
31/01/22 → 30/07/27
Project: Research council
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