The field of mineral processing and materials engineering is witnessing significant advancements with the integration of AI and machine learning techniques. Researchers are developing innovative approaches to optimize mineral processing operations under uncertainty, using methods such as Partially Observable Markov Decision Processes (POMDP) and physics-informed machine learning. These approaches have the potential to improve the efficiency and productivity of mineral processing circuits, while also reducing uncertainty and improving decision-making. Noteworthy papers in this area include:

• AI-Driven Optimization under Uncertainty for Mineral Processing Operations, which demonstrates the capabilities of a POMDP approach in handling feedstock and process model uncertainty.
• Opening the Black Box: An Explainable, Few-shot AI4E Framework Informed by Physics and Expert Knowledge for Materials Engineering, which presents an explainable AI framework that provides quantitative predictions and physical insight into materials engineering processes.
• Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling, which introduces a computational framework that combines physical insights with machine learning to develop a physics-informed continuous cooling transformation (CCT) model for steels.
• Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization, which introduces a new strategy for detecting cracks in 2D solids using holomorphic neural networks and genetic optimization.