ICEAF IX conference invites researchers, engineers, and academics to contribute to a special session dedicated to exploring the applications of data analytics and artificial intelligence, specifically machine learning, in engineering. This special session aims to bring together experts from academia and industry to share insights, methodologies, and advancements in leveraging data-driven approaches for enhancing materials design, predicting fatigue and fracture behavior, and estimating the properties of engineering materials. Special session will provide a platform for discussions on the latest advancements and future directions in this rapidly evolving field.

Topics of interest include but are not limited to:

• Data-driven materials design and optimization
• Predictive modeling for fatigue and fracture analysis
• Estimation of materials' behavior and properties through machine learning
• Novel applications of data analytics in engineering alloys and metals
• Multiscale approaches integrating data analytics for mechanical behavior understanding
• Virtual testing, digital twins, and AI implementation in materials engineering
• Methodological frameworks for machine learning applications in engineering