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• W3178027639 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." @default.
• W3178027639 created "2021-07-19" @default.
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• W3178027639 date "2021-07-14" @default.
• W3178027639 modified "2023-09-26" @default.
• W3178027639 title "Feed-Forward Neural Networks for Failure Mechanics Problems" @default.
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• W3178027639 doi "https://doi.org/10.3390/app11146483" @default.
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