Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 6483 |
| Seitenumfang | 22 |
| Fachzeitschrift | Applied Sciences |
| Jahrgang | 11 |
| Ausgabenummer | 14 |
| Publikationsstatus | Veröffentlicht - 14 Juli 2021 |
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.
ASJC Scopus Sachgebiete
- Werkstoffwissenschaften (insg.)
- Physik und Astronomie (insg.)
- Instrumentierung
- Ingenieurwesen (insg.)
- Chemische Verfahrenstechnik (insg.)
- Prozesschemie und -technologie
- Informatik (insg.)
- Angewandte Informatik
- Chemische Verfahrenstechnik (insg.)
- Fließ- und Transferprozesse von Flüssigkeiten
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in: Applied Sciences, Jahrgang 11, Nr. 14, 6483, 14.07.2021.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Feed-forward neural networks for failure mechanics problems
AU - Aldakheel, Fadi
AU - Satari, Ramish
AU - Wriggers, Peter
N1 - Funding Information: Funding: The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover (LUH-TIB).
PY - 2021/7/14
Y1 - 2021/7/14
N2 - 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.
AB - 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.
KW - Elasticity
KW - Failure mechanics
KW - Neural networks (NNs)
KW - Phase-field modeling
UR - http://www.scopus.com/inward/record.url?scp=85111146427&partnerID=8YFLogxK
U2 - 10.3390/app11146483
DO - 10.3390/app11146483
M3 - Article
AN - SCOPUS:85111146427
VL - 11
JO - Applied Sciences
JF - Applied Sciences
SN - 2076-3417
IS - 14
M1 - 6483
ER -