Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
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 compared: Full Fine-Tuning (FFT), Lightweight Fine-Tuning (LFT), and Low-Rank Adaptation (LoRA). The central finding is that pretrained geometry enc...
| Authors | Seunghwan Keum, Alok Warey |
|---|---|
| Primary category | cs.CE |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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