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

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 Convolutional Neural Net model respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/17/2022

Graph Neural Network-based Surrogate Models for Finite Element Analysis

Current simulation of metal forging processes use advanced finite elemen...
research
11/14/2018

How to get meaningful and correct results from your finite element model

This document gives guidelines to set up, run, and postprocess correct s...
research
05/23/2020

Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns

This paper uses the peridynamic theory, which is well-suited to crack st...
research
06/04/2021

Point Cloud Failure Criterion for Composites using k-Nearest Neighbor Classification

Numerous theories of failure have been postulated and implemented in var...
research
11/01/2022

MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations

In many cutting-edge applications, high-fidelity computational models pr...
research
04/17/2019

3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models

In this study, we present an analysis of model-based ensemble learning f...
research
04/09/2019

Thermomechanical modelling of ceramic pressing and subsequent sintering

An elastic-visco-plastic thermomechanical model for the simulation of co...

Please sign up or login with your details

Forgot password? Click here to reset