Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning

arXiv:2605.27968v1 Announce Type: cross Abstract: Deploying Scientific Machine Learning surrogates in industrial CFD workflows requires adapting pretrained models to new vehicle families without large datasets; yet whether geometric representations learned by a geometry encoder transfer to topologically distinct shapes remains unvalidated. We address this through leave-one-family-out experiments on a 61.47M-parameter Transformer surrogate (AB-UPT) pretrained on four vehicle families (411 external aerodynamics cases) and adapted to the held-out fifth with only 20 samples. Three strategies are c

Source: arXiv cs.LG — read the full report at the original publisher.

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