论文标题
基于点云的有限元分析的深度学习模型
Point-Cloud-based Deep Learning Models for Finite Element Analysis
论文作者
论文摘要
在本文中，我们探讨了基于点云的深度学习模型，以分析有限元分析引起的数值模拟。目的是自动对模拟的结果进行分类，而无需乏味的人类干预。这里介绍了两个模型：点网格分类模型和动态图卷积神经网模型。两种受过训练的点云深度学习模型在实验上表现良好，并具有由汽车行业引起的有限元分析。提出的模型在自动化有限元模拟的分析过程方面显示了希望。对于点网和动态图卷积神经网模型，获得的精度分别为79.17％和94.5％。
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.
