Estimate crash and flow behaviour of early variants

Manufacturers estimate the crash and flow behaviour of early design variants with AI models before running costly full calculations

Needs setupAI Act: no extra obligationsDesigning & simulatingAutomotive

Fits: Research & development

An AI surrogate model, trained on your previous simulations, estimates in seconds how a new design variant is likely to behave in a crash or in the airflow. This means you only run costly full calculations for the promising variants and can test far more ideas in early phases.

Is this for you?

  • From earlier projects you have many simulation runs with the same parameters, such as intrusion, acceleration or drag coefficient.
  • A full crash or flow calculation takes hours to days and slows down the early concept phase.
  • Your simulation and testing teams can support a surrogate model and check it against real calculations.
First step today

Take a small table of earlier simulation runs for one component, replace parameter names and values with neutral labels or normalised figures, and use the template below to check how well a simple surrogate model predicts runs it has not seen.

How to go about it

  1. Define the component, load case and target variables
  2. Prepare previous simulations and tests
  3. Train the surrogate model and have variants evaluated
  4. Select promising variants on technical grounds
  5. Confirm with full calculation and testing
  6. Add new runs to the surrogate model

Templates to copy

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Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

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Same approach, other use cases

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AT-07 · Legal status 08.10.2026