一致性(知识库)
过程(计算)
数据驱动
数据科学
系统工程
风险分析(工程)
人工智能
机器学习
计算机科学
工程类
医学
操作系统
作者
Haijie Wang,Bo Li,Jing Gong,Fu‐Zhen Xuan
标识
DOI:10.1016/j.engfracmech.2023.109242
摘要
Fatigue life prediction is critical for ensuring the safe service and the structural integrity of mechanical structures. Although data-driven approaches have been proven effective in predicting fatigue life, the lack of physical interpretation hinders their widespread applications. To satisfy the requirements of physical consistency, hybrid physics-informed and data-driven models (HPDM) have become an emerging research paradigm, combining physical theory and data-driven models to realize the complementary advantages and synergistic integration of physics-based and data-driven approaches. This paper provides a comprehensive overview of data-driven approaches and their modeling process, and elaborates the HPDM according to the combination of physical and data-driven models, then systematically reviews its application in fatigue life prediction. Additionally, the future challenges and development directions of fatigue life prediction are discussed.
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