Feed-Forward Neural Networks for Failure Mechanics Problems
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 particula...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6483 |
| _version_ | 1827687943424180224 |
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| author | Fadi Aldakheel Ramish Satari Peter Wriggers |
| author_facet | Fadi Aldakheel Ramish Satari Peter Wriggers |
| author_sort | Fadi Aldakheel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:46:02Z |
| format | Article |
| id | doaj.art-7fe29337a8bb4f98826f559f8548151a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:46:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7fe29337a8bb4f98826f559f8548151a2023-11-22T03:10:28ZengMDPI AGApplied Sciences2076-34172021-07-011114648310.3390/app11146483Feed-Forward Neural Networks for Failure Mechanics ProblemsFadi Aldakheel0Ramish Satari1Peter Wriggers2Institute of Continuum Mechanics, Leibniz University Hannover, 30823 Garbsen, GermanyInstitute of Continuum Mechanics, Leibniz University Hannover, 30823 Garbsen, GermanyInstitute of Continuum Mechanics, Leibniz University Hannover, 30823 Garbsen, GermanyThis 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.https://www.mdpi.com/2076-3417/11/14/6483neural networks (NNs)elasticityfailure mechanicsphase-field modeling |
| spellingShingle | Fadi Aldakheel Ramish Satari Peter Wriggers Feed-Forward Neural Networks for Failure Mechanics Problems Applied Sciences neural networks (NNs) elasticity failure mechanics phase-field modeling |
| title | Feed-Forward Neural Networks for Failure Mechanics Problems |
| title_full | Feed-Forward Neural Networks for Failure Mechanics Problems |
| title_fullStr | Feed-Forward Neural Networks for Failure Mechanics Problems |
| title_full_unstemmed | Feed-Forward Neural Networks for Failure Mechanics Problems |
| title_short | Feed-Forward Neural Networks for Failure Mechanics Problems |
| title_sort | feed forward neural networks for failure mechanics problems |
| topic | neural networks (NNs) elasticity failure mechanics phase-field modeling |
| url | https://www.mdpi.com/2076-3417/11/14/6483 |
| work_keys_str_mv | AT fadialdakheel feedforwardneuralnetworksforfailuremechanicsproblems AT ramishsatari feedforwardneuralnetworksforfailuremechanicsproblems AT peterwriggers feedforwardneuralnetworksforfailuremechanicsproblems |