This is like having a super-fast ‘wind tunnel in a box’ for data centers. Instead of waiting hours or days for detailed physics simulations to tell you where the hot spots will be in a server room, a learned surrogate model gives you almost-instant 3D temperature predictions so you can test many cooling and layout ideas very quickly.
Traditional CFD-based thermal simulations for data centers are slow and expensive, which makes it hard to iterate on room layout, rack placement, and cooling strategies. This surrogate modeling approach promises much faster 3D thermal prediction, enabling more design iterations, better energy efficiency, and reduced risk of thermal failures.
If trained on proprietary historical data center telemetry and CFD runs, the surrogate can become a highly tuned, domain-specific asset that is hard to replicate, embedded directly into design workflows for ongoing optimization.
Open Source (Llama/Mistral)
Unknown
High (Custom Models/Infra)
Training cost and data availability for high-fidelity 3D simulations; GPU memory and latency constraints for high-resolution 3D inference.
Early Adopters
Focuses specifically on fast 3D surrogate modeling for thermal management in data centers, targeting very high-fidelity thermal behavior with substantially lower computation than full CFD, which distinguishes it from generic building simulation or traditional CFD-only workflows.
2 use cases in this application