Thermal Simulation

Thermal simulation aims to accurately and efficiently predict temperature distributions in various systems, from electronic chips and batteries to buildings and spacecraft, primarily to optimize design, control, and safety. Current research emphasizes the development of hybrid models, combining physics-based equations with machine learning techniques like neural networks (including physics-informed neural networks and deep operator networks), and multi-fidelity approaches that integrate data from different simulation sources. These advancements offer significant potential for improving the speed and accuracy of thermal simulations, leading to more efficient designs and better control strategies across diverse engineering applications.