Thermal Behavior

Thermal behavior modeling is undergoing a transformation driven by the need for faster, more accurate predictions across diverse applications, from spacecraft design to medical procedures and additive manufacturing. Current research heavily emphasizes hybrid models, combining the strengths of physics-based simulations (e.g., finite-element methods) with machine learning techniques like neural networks (including physics-informed neural networks and graph neural networks) to achieve real-time performance and improved generalization. This focus on efficient and accurate thermal modeling is crucial for optimizing designs, enhancing process control, and enabling advanced functionalities in various fields, ultimately leading to improved efficiency and safety.