The field of thermal management and predictive modeling is witnessing significant advancements, driven by the increasing demand for efficient and reliable systems. Researchers are exploring innovative approaches to predict thermal behavior, detect anomalies, and improve overall system performance. Notably, the integration of machine learning and physics-based modeling is gaining traction, enabling the development of more accurate and robust predictive models.

One of the key areas of focus is the development of lightweight and efficient models that can be deployed on resource-constrained devices. This is particularly important for applications such as battery management systems and robot joint motors, where real-time prediction and monitoring are crucial.

Another significant trend is the use of digital twins and generative AI to manage thermally anomalous and generate uncritical robot states. This approach has shown promising results in predicting and anticipating thermal feasibility of desired motion profiles, which is essential for ensuring human safety and robot availability.

Some noteworthy papers in this area include: KAN-Therm, which proposes a lightweight battery thermal model using Kolmogorov-Arnold networks, achieving the best prediction accuracy with the least memory overhead and computation time. KAN-SR, which introduces a novel symbolic regression framework built on Kolmogorov-Arnold networks, recovering ground-truth equations and modeling dynamics of in-silico bioprocess systems precisely.