Point-Cloud-based Deep Learning Models for Finite Element Analysis

TL;DR

This paper introduces two point-cloud deep learning models, Point-Net and Dynamic Graph CNN, to automate the classification of finite element analysis results, achieving high accuracy and promising automation in simulation analysis.

Contribution

The paper presents novel application of point-cloud deep learning models for automating finite element analysis classification tasks.

Findings

Point-Net achieved 79.17% accuracy.

Dynamic Graph CNN achieved 94.5% accuracy.

Models performed well on automotive industry simulations.

Abstract

In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. The objective is to classify automatically the results of the simulations without tedious human intervention. Two models are here presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model. Both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. The proposed models show promise in automatizing the analysis process of finite element simulations. An accuracy of 79.17% and 94.5% is obtained for the Point-Net and the Dynamic Graph Convolutional Neural Net model respectively.