Feed-forward neural networks for failure mechanics problems

authored by
Fadi Aldakheel, Ramish Satari, Peter Wriggers
Abstract

This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.

Organisation(s)
Institute of Continuum Mechanics
Type
Article
Journal
Applied Sciences
Volume
11
No. of pages
22
ISSN
2076-3417
Publication date
14.07.2021
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Materials Science(all), Instrumentation, Engineering(all), Process Chemistry and Technology, Computer Science Applications, Fluid Flow and Transfer Processes
Electronic version(s)
https://doi.org/10.3390/app11146483 (Access: Open)
 

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