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PreprintCC BY 4.0arXiv

Application of Machine Learning to the Computation of Parameters in the SUPG Method for Unstructured and Anisotropic Meshes

  • Petr Knobloch
  • Manoj Prakash
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Abstract

Solution of convection-dominated problems using stabilized methods often requires to specify stabilization parameters whose optimal choice is not known but which considerably influence the quality of the approximate solution. In a recent work of the authors, an approach for computing these parameters by machine learning was proposed and applied to the streamline upwind/Petrov-Galerkin (SUPG) method for convection-diffusion equations. In the present paper, this approach is studied numerically for unstructured meshes and extended to meshes obtained by anisotropic mesh adaptation.

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Cite

BibTeXRIS
BibTeX
@misc{knobloch2026application,
  title = {{Application of Machine Learning to the Computation of Parameters in the SUPG Method for Unstructured and Anisotropic Meshes}},
  author = {Petr Knobloch and Manoj Prakash},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.32358},
  eprint = {2609.32358},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.32358},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Application of Machine Learning to the Computation of Parameters in the SUPG Method for Unstructured and Anisotropic Meshes
AU  - Petr Knobloch
AU  - Manoj Prakash
PY  - 2026
DA  - 2026/09/26
PB  - arXiv
DO  - 10.48550/arxiv.2609.32358
UR  - https://arxiv.org/abs/2609.32358
AB  - Solution of convection-dominated problems using stabilized methods often requires to specify stabilization parameters whose optimal choice is not known but which considerably influence the quality of the approximate solution. In a recent work of the authors, an approach for computing these parameters by machine learning was proposed and applied to the streamline upwind/Petrov-Galerkin (SUPG) method for convection-diffusion equations. In the present paper, this approach is studied numerically for unstructured meshes and extended to meshes obtained by anisotropic mesh adaptation.
ER  -