PhD Student Deven Patel’s Paper Has Been Selected for the Cover Page of the Journal of Annual Review of Heat Transfer
PhD student Deven Patel’s recent paper, “Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing,” has been selected to be put on the cover page of the Journal of Annual Review of Heat Transfer (ARHT) volume — Machine Learning and Additive Manufacturing.
ARHT reflects all aspects of heat transfer and fluid flow as depicted by an array of the top international specialists in many fields. This paper presents a comprehensive evaluation of modeling strategies for microstructure prediction in metal additive manufacturing (AM).
The strengths and limitations of experimental, computational, and data-driven methods are analyzed in depth, and highlight recent advances in hybrid physics-informed machine learning (PIML) frameworks that integrate physical knowledge with ML. Key challenges, such as data scarcity, multi-scale coupling, and uncertainty quantification, are discussed alongside future directions.
Ultimately, this assessment underscores the importance of PIML-based hybrid approaches in enabling predictive, scalable, and physically consistent microstructure modeling for site-specific, microstructure-aware process control and the reliable production of high-performance AM components.
