Feed-forward neural networks for failure mechanics problems
- verfasst von
- 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.
- Organisationseinheit(en)
-
Institut für Kontinuumsmechanik
- Typ
- Artikel
- Journal
- Applied Sciences
- Band
- 11
- Anzahl der Seiten
- 22
- ISSN
- 2076-3417
- Publikationsdatum
- 14.07.2021
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Werkstoffwissenschaften (insg.), Instrumentierung, Ingenieurwesen (insg.), Prozesschemie und -technologie, Angewandte Informatik, Fließ- und Transferprozesse von Flüssigkeiten
- Elektronische Version(en)
-
https://doi.org/10.3390/app11146483 (Zugang:
Offen)
