Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods

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 datadriven models to realize the complementary advantages and synergistic integration of physicsbased and data-driven approaches. This paper provides a comprehensive overview of datadriven 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.