Simulations were stuck in 2D and took hours. Physics-informed AI now models 3D urban airflow in seconds.
An aerospace engineering research laboratory focused on fluid mechanics and urban airflow simulations for climate resilience.
Traditional computational fluid dynamics methods forced a trade-off between accuracy and speed, restricting models to 2D planes that could not...
“Aerodynamics and turbulence are unsolved problems of physics. The answer must be in the data, so we were inspired and intrigued by using AI methods to really interrogate this data, and that’s the type of solutions we have been obtaining to develop very efficient frameworks.”
Public research university with comprehensive academic and graduate programs.
Provided hands-on engineering assistance for NVIDIA frameworks to the VinuesaLab research team.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
University of Michigan's Urban flow simulation is part of this use case:
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Simulations were stuck in 2D and took hours. Physics-informed AI now models 3D urban airflow in seconds.
An aerospace engineering research laboratory focused on fluid mechanics and urban airflow simulations for climate resilience.
Traditional computational fluid dynamics methods forced a trade-off between accuracy and speed, restricting models to 2D planes that could not...
“Aerodynamics and turbulence are unsolved problems of physics. The answer must be in the data, so we were inspired and intrigued by using AI methods to really interrogate this data, and that’s the type of solutions we have been obtaining to develop very efficient frameworks.”
Public research university with comprehensive academic and graduate programs.
Provided hands-on engineering assistance for NVIDIA frameworks to the VinuesaLab research team.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
University of Michigan's Urban flow simulation is part of this use case:
Related implementations across industries and use cases
Researchers had just 30-60 minutes to warn of severe hailstorms. Now, an AI radar model generates high-resolution forecasts in minutes.
Data policies blocked collaboration. Federated AI detects signals 10x faster, syncing global observatories without exposing raw data.
Detectors upgraded to 100,000 images per second left annotators drowning. AI now delivers labeled 3D volumes live, at the beamline.
QC simulations ran 15 hours and reset from scratch on every change. Digital twins cut that to 3.6 seconds.
Reconstruction cost capped how much fleet data Li Auto could simulate. A unified camera-LiDAR model halved cost and doubled scene coverage.
Task-by-task, manual mapping couldn't keep pace with national recovery targets. AI now maps peatlands and tracks restoration across England.
High incident volumes across complex IT/OT networks overwhelmed security teams. Now, AI agents triage alerts so analysts can hunt threats.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.