  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.
