GPStudio - Generative Physics for Engineering

GPStudio* is a generative physics AI platform* that gives engineering teams full fidelity simulation results in seconds instead of days. For the first time, AI has learned the laws of physics themselves, not snapshots of past simulations. GPStudio is powered by foundational models trained on the fundamental principles of physics. Because the models learn the causality of physical behaviour rather than static results, they generalise to geometries and operating conditions they have never encountered. This is not a faster solver. It is a boundless design space where discovery and validation happen together, opening up territory that conventional tools never had the time to reach.

What you get:

Engineering grade aerodynamics, thermal management, and structural analysis from a suite of pretrained models, with no retraining required for each new problem. Results validated against full fidelity CFD, with predictions within 2% of CFD accuracy across automotive, aerospace, electronics, and data center applications. Meshing and solver setup removed entirely, cutting simulation from days to minutes. Rapid design space exploration, so teams iterate across thousands of candidate designs in the time it once took to validate one.

Built for AWS:

• Deploys on Amazon EC2 GPU instances, including P5 and P4d, for high throughput inference and training.
• Scales across EC2 UltraClusters and Auto Scaling groups for large workloads.
• Integrates with Amazon S3 for training data, geometry, and results storage.
• Works alongside Amazon SageMaker for model lifecycle and experiment management.
• Supports private deployment within your own VPC for data residency and IP protection.

Already proven and in production with global OEMs, Tier 1 suppliers, Formula 1 teams, and Fortune 500 manufacturers. Founded 2022 and based in London, UK. Backed by Insight Partners, UP.Partners, Cambridge Innovation Capital, and InMotion Ventures, with a team drawn from Google DeepMind, Palantir, NASA, Cambridge, Oxford, and Imperial.