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• W3211723343 abstract "Physics-informed neural network has strong generalization ability for small dataset, due to the inclusion of underlying physical knowledge. Two strategies are enforced to incorporate physics constraints to a deep neural network in this work. One is to obtain extended features through physics-informed feature engineering, and the other is to incorporate physics-informed loss function into deep neural network as constraints. Conventional machine learning models, deep neural network and physics-informed neural network are applied to predict creep-fatigue life of 316 stainless steel. Results show that physics-informed neural network presents better prediction accuracy than deep neural network and conventional machine learning models." @default.
• W3211723343 created "2021-11-22" @default.
• W3211723343 creator A5018627197 @default.
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• W3211723343 date "2021-12-01" @default.
• W3211723343 modified "2023-10-05" @default.
• W3211723343 title "A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures" @default.
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• W3211723343 doi "https://doi.org/10.1016/j.engfracmech.2021.108130" @default.
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