Evaluating the Quality of Finite Element Meshes with Machine Learning
Not on the map. Why?
Abstract (arXiv)
This paper addresses the problem of evaluating the quality of finite element meshes for the purpose of structural mechanic simulations. It proposes the application of a machine learning model trained on data collected from expert evaluations. The task is characterised as a classification problem, where quality of each individual element in a mesh is determined by its own properties and adjacency structures. A domain-specific, yet simple representation is proposed such that off-the-shelf machine learning methods can be applied. Experimental data from industry practice demonstrates promising results.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 22 Jul 2021.
Builds on
4 of 19 references.
References · 4
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2015Gated Graph Sequence Neural Networkscited by 796
- 2020Learning Visual Commonsense for Robust Scene Graph Generationcited by 47
- 2019Network Datacited by 9
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.