文献类型：期刊文献

英文题名：A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures

作者：Zhang, Xiao-Cheng[1];Gong, Jian-Guo[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：258

外文期刊名：ENGINEERING FRACTURE MECHANICS

收录：;EI(收录号：20214811225747);WOS:【SCI-EXPANDED(收录号:WOS:000722150300001)】；

基金：Supports from National Natural Science Foundation of China (52175139, 51835503) are greatly acknowledged.

语种：英文

外文关键词：Machine learning; Deep neural network; Physics-informed; Creep-fatigue; Life prediction

摘要：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.