Our data center HVAC research leverages advanced AI-driven approaches combined with physics-based modeling to revolutionize the design, optimization, and control of next-generation cooling systems. By integrating experimental testing, finite element modeling, machine learning-based surrogate modeling, and physics-informed optimization, we enable the development of intelligent HVAC systems that maximize energy efficiency while ensuring optimal thermal performance for data center operations and chip-level cooling applications.
HVAC System Optimization
We develop advanced AI-driven approaches combined with physics-based modeling for optimizing data center HVAC systems. Our research focuses on intelligent control strategies, energy-efficient operation, and thermal management using machine learning-based surrogate modeling and physics-informed optimization techniques to achieve optimal performance while minimizing energy consumption.
Chip Cooling
We leverage AI and physics-based modeling to optimize chip-level cooling systems for high-performance computing applications. Our research focuses on developing intelligent cooling solutions using finite element modeling, machine learning-based thermal prediction, and physics-informed design optimization to ensure reliable thermal management of semiconductor devices under various operating conditions.