Understanding the fatigue behavior and properties of materials and structures is essential for ensuring their safety in engineering applications. Fatigue studies often require complex simulations, which can be resource-intensive and time-consuming. Recently, machine learning (ML) techniques have demonstrated significant potential in advancing fatigue analysis through accurate and efficient predictions. 
This session seeks to present cutting-edge research on applying machine learning approaches to investigate mechanical behaviors and properties related to fatigue. Topics of interest include, but are not limited to the following:

• Development of machine learning models for predicting fatigue life and damage evolution
• Data-driven analysis of mechanical properties under complex loading conditions
• Fracture surface and microstructure analysis utilizing machine learning methods
• Case studies demonstrating successful machine learning applications in fatigue analysis of components and structures 

We invite submissions from academia and industry that highlight innovative strategies and practical applications of machine learning in the field of fatigue research. We look forward to fostering discussions on the latest advancements and future directions in this exciting area.