Physics AI · automotive aerodynamics

The world's largest,
most diverse
car design dataset.

One million production-feasible vehicle shapes. CFD-ready. Owned to the last vertex. The ground truth physics AI has been waiting for.

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Why data decides

Models create potential.
Data creates trust.
Turn physics AI from prediction
into engineering confidence.

With the right data,
it gets its ChatGPT moment.

Language models became intelligent the day they met the right corpus at scale. Physics AI has been waiting for its corpus. We built it.

Film 01
From one shape, the entire design space.
Quality-controlled by design

Not random morphs.
Cars that could actually be built.

Anyone can stretch a mesh until it looks new. We don't.

The industry default

Parameter noise

Engineers who have never designed a car perturb surfaces until the meshes differ. The results look plausible — and could never leave a studio, let alone a factory. A model trained on noise learns to guess.

The Formdesign Labs way

Designed variation

Car designers shape every variation: real proportions, real surfacing, aero intent, production feasibility. Quality-controlled ground truth — the only data that trains a foundation model you can trust.

The corpus

Scale, with standards.

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Vehicle shapes
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Base architectures
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Vehicle categories
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Proprietary · clean to train on
Neural scaling law

Scaling law.
More data improves model accuracy.

biggest gains diminishing returns Prediction error More diverse training data → ModelError ∝ N−0.5
More diverse CFD training data → lower prediction error, with diminishing returns.
Foundation-model scale

The dataset a
physics-AI foundation model needs.

ImageNet gave computer vision the corpus that made foundation models possible. One million CFD-ready geometries brings that scale to the physical world — the corpus to pretrain a foundation model that predicts real-world physics.

Buy the data.
Own the ChatGPT moment
of physics AI.

Skip the years of corpus-building. Spend them training — and lead.

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