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Quality & Testing · Prototype
Surrogate Modeling / ML Surrogates
Approximate test-response prediction before full validation
Live Core high effect
Core capability
The system can evaluate large numbers of design alternatives quickly, making broad exploration and screening economically realistic where full simulation would be too slow or expensive.
How it works
Once trained on historical simulation results, the system can estimate key performance metrics from design inputs very quickly, which makes large-scale screening and comparison much more practical.
Application here
A fast model estimates how a prototype is likely to perform in tests before the actual tests are run.
Business impact
This supports better test planning and earlier identification of likely problem areas, saving time and test resources.
Limitations
These predictions carry significant uncertainty and must not replace physical validation for critical parameters or decisions.
In production
This already enables teams to explore far more design options than full simulation alone would allow in the same time and budget.
Research
The frontier is toward fast models that not only predict quickly, but also show where their answers are reliable and where detailed simulation is still necessary.
Examples
SIA Simulation Numérique 2025 Challenge: Réduction de Modèle
Description: Benchmark using crash test data from Renault and Stellantis. Surrogate models (ReCUR and neural fields) trained on 60 simulations varying crashbox thicknesses predict structural displacements. Models enable design exploration and optimization, reducing high-fidelity sim needs from 60 to 20 runs while maintaining ranking accuracy for safety KPIs.



Introducing Surrogate Models to the Structural Preliminary Aircraft Design Phase
Description: Researchers at Technical University Berlin built kriging surrogate from FEM of cantilever wing (ribs 5-19, shell 2-3.4mm). Model predicts stress/weight with 1% error vs high-fidelity at test points, enabling rapid optimization of structural weight under loads, before full aircraft FEM validation.
https://miurasimulation.com/use-of-scarce-crash-simulation-data-to-build-efficient-surrogate-models/https://www.dglr.de/publikationen/2018/480173.pdf
Sources
Ansys SimAI — ; Ansys optiSLang —
https://www.ansys.com/products/ai/ansys-simaihttps://www.ansys.com/products/connect/ansys-optislang