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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