AI for Thermal Management of Electronic Systems: A Pathway to Digital Twins

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The rapid rise in power density and complexity of electronic systems has made thermal management a critical challenge for ensuring reliability, performance, and sustainability. Artificial Intelligence (AI) offers transformative opportunities to address this challenge by enabling data-driven modeling, optimization, and predictive control of cooling systems. By integrating AI with experimental and physics-based approaches, adaptive models can be developed to capture transient thermal behaviors, and optimize system-level energy efficiency. This forms the foundation for digital twins, virtual replicas that continuously interact with their physical counterparts to provide system specific real-time monitoring, and data driven decision support. In this talk, I will present recent and ongoing research activities at ES2 Binghamton on AI-enabled thermal management design, with emphasis on cooling solutions for high-power chips in data centers. I will further highlight how these developments serve as a pathway towards creating digital twins, dynamic virtual replicas that integrate real-time data, physics, and AI to enable system-level monitoring, prediction, and optimization. Together, these advancements pave the way for reliable, energy-efficient, and sustainable electronic systems.