Transforming Future Data Center Operations and Management via Physical AI

Data centers (DCs) are critical for artificial intelligence (AI) and the digital economy, with AIDCs introducing new operational challenges. This research proposes a novel Physical AI (PhyAI) framework to advance DC operations and management. The system features three core modules: an industry-grade in-house DC simulation engine for high-fidelity AIDC modeling, an AI engine built on NVIDIA PhysicsNeMo for training and evaluating physics-informed machine learning (PIML) models, and a digital twin platform based on NVIDIA Omniverse. This framework enables the creation ofreal-timedigital twins to digitalize, optimize, and automate future DC operations. A case study demonstrated its effectiveness in predicting thermal and airflow profiles for a large-scale DC in real-time, achieving a median absolute temperature prediction error of 0.18 °C, outperforming traditional CFD/HT simulations. This emerging approach would open doors to several potential research directions for advancing PhyAI in future DC operations.