Evaluating the Quality of Finite Element Meshes with Machine Learning

TL;DR

This paper introduces a machine learning approach to evaluate finite element mesh quality for structural simulations, using expert-labeled data and simple element representations to classify mesh elements effectively.

Contribution

It presents a novel machine learning framework for mesh quality assessment that leverages expert data and simple representations, enabling practical application in industry.

Findings

Promising classification accuracy on industry data

Effective use of simple, domain-specific features

Potential for automated mesh quality evaluation

Abstract

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.