Application of Machine Learning to the Computation of Parameters in the SUPG Method for Unstructured and Anisotropic Meshes
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Knobloch, Petr, Prakash, Manoj
Institution: Charles University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Univerzita Karlova v Praze
OpenAlex ID: W7214725493