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research article

How to teach neural networks to mesh: Application on 2-D simplicial contours

Papagiannopoulos, Alexis  
•
Clausen, Pascal
•
Avellan, François  
2021
Neural Networks

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of vertices to be inserted inside the contour cavity, their location, and connectivity. The accuracy of the scheme is evaluated by comparing the quality of the mesh generated by the neural networks with that generated by a reference mesher. Based on an element quality metric, after conducting tests on contours for a various number of edges, the results show a maximum average deviation of 15.2% on the mean quality and 27.3% on the minimum quality between the elements of the meshes generated by the scheme and the ones generated from the reference mesher; the scheme is able to produce good quality meshes that are suitable for meshing purposes. The meshing scheme is also applied to generate larger scale meshes with a recursive implementation. The findings encourage the adaption of the scheme for 3-D mesh generation.

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Type
research article
DOI
10.1016/j.neunet.2020.12.019
Author(s)
Papagiannopoulos, Alexis  
Clausen, Pascal
Avellan, François  
Date Issued

2021

Published in
Neural Networks
Volume

136

Start page

152

End page

179

Subjects

Mesh generation

•

Simplicial mesh

•

Neural networks

•

Machine learning

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LMH  
FunderGrant Number

FNS-NCCR

Project Grant No. PZENP2_166865

Available on Infoscience
April 9, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/177064
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