A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures. (December 2021) - Zhang, Xiao-Cheng; Gong, Jian-Guo; Xuan, Fu-Zhen
Highlights: Physics-informed neural network (PINN) is built for creep-fatigue life prediction. Physics-informed feature engineering and physics-informed loss function are applied. PINN exhibits better prediction capacity than conventional machine learning models. 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.