This is like a flight simulator for datacenter cooling: instead of running a slow, physics-heavy simulation every time you move a rack or change airflow, a trained AI model instantly estimates the 3D temperature in the whole room.
Engineers need to understand and optimize airflow and temperature distribution in large datacenter rooms, but full 3D CFD simulations are extremely slow and expensive. The hybrid ANN–CNN surrogate model gives near-instant temperature predictions, enabling faster design iterations, capacity planning, and thermal risk checks.
Domain-specific training data and validation on realistic datacenter configurations, plus integration into existing thermal design workflows, can create a defensible surrogate modeling capability for specific facilities or portfolios.
Classical-ML (Scikit/XGBoost)
Unknown
High (Custom Models/Infra)
Model fidelity vs. training data coverage (risk of poor generalization to novel room configurations) and potential retraining cost when hardware layout or cooling technology changes significantly.
Early Adopters
Unlike generic CFD solvers, this approach uses a tailored ANN–CNN hybrid network as a surrogate to approximate full 3D temperature fields, dramatically reducing computation time while preserving useful spatial detail for datacenter thermal design and optimization.