The field of thermal modeling and control is moving towards the development of more accurate and efficient methods for estimating thermal properties and power losses. Researchers are exploring the use of data-driven techniques, such as machine learning and Bayesian inference, to improve the accuracy of thermal models and reduce the need for direct measurements. Additionally, there is a growing interest in the development of physics-informed machine learning models that can leverage the strengths of both physical models and data-driven approaches. Notable papers in this area include the use of Bayesian techniques for estimating thermal properties and boundary heat transfer coefficients, and the development of hybrid frameworks that combine physics-based thermal modeling with data-driven techniques for power loss identification. The paper on in-context learning for zero-shot speed estimation of BLDC motors also presents an innovative approach to sensorless control. Furthermore, the review of physics-informed machine learning for building performance simulation provides a comprehensive overview of the current state of the field and identifies key challenges and future research directions.