Thermal Control

Thermal control research focuses on efficiently managing temperature in diverse systems, from data centers and buildings to micro-devices and large language models, aiming to optimize energy use, performance, and component lifespan. Current research employs various machine learning approaches, including model predictive control, deep reinforcement learning, and physics-informed neural networks, often coupled with optimization algorithms like ADMM and DRO, to achieve precise and adaptable temperature regulation. These advancements have significant implications for energy efficiency in various sectors and enable the development of more robust and reliable systems across diverse applications.