Surrogate · Automotive
Gaussian Process Regression for Automotive
Apply the gaussian process regression to real automotive problems — in the browser, with AI assistance.
GPs model outputs as draws from a distribution over functions, giving both predictions and calibrated uncertainty for expensive simulations.
In automotive, teams face challenges like drag and fuel economy, crash safety, battery thermal management. The gaussian process regression directly supports use cases such as external aerodynamics, crashworthiness fea, ev battery cooling, and PolySim's AI Copilot can recommend settings and catch common setup errors before you run.
Typical Automotive use cases
- External aerodynamics
- Crashworthiness FEA
- EV battery cooling
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