Vehicle Surface Pressure Prediction from 2D Sketches via a Pre-Trained Diffusion Model

In the early stages of automotive design, designers explore shape concepts using 2D sketches, yet existing aerodynamic evaluation methods require 3D geometry representations. We propose an end-to-end approach that directly predicts surface pressure coefficient (Cp) distributions from 2D sketch images via image-to-image translation. By fine-tuning a pre-trained image editing diffusion model (Qwen-Image-Edit-2511) with LoRA on the large-scale automotive aerodynamics dataset DrivAerNet++, we achieve Relative L2 Error (Rel L2) = 0.165 and R$^2$ = 0.955 on the test set. Even with training data reduced to 2.2\% (128 samples), the model maintains Rel L2 = 0.218, demonstrating applicability in practical settings where CFD simulation costs are prohibitive. We confirm good generalization to unseen base vehicle categories while revealing that prediction quality degrades for shape features absent from training data, highlighting that coverage of shape features matters more than category labels. We also investigate calibrated uncertainty (UQ) via diffusion ensemble, revealing when variance reliably indicates prediction error. These results demonstrate the feasibility of aerodynamic evaluation without 3D geometry representations, opening a path toward aerodynamic feedback at the earliest design stage.