AeroAgent: A Vision–Physics–Decision Framework for Aerodynamic Vehicle Design

Modern generative models can propose striking 3D vehicle shapes from text and images, but turning these sketches intoaerodynamically efficient, regulation-compliant designs still requires weeks of high-fidelity computational fluiddynamics (CFD) and manual iteration. As a result, fast 3D generation without trustworthy physics in the loop doeslittle to reduce end-to-end design time. We study how an AI agent can close this loop under a strict CFD budget.We introduce AeroAgent, a vision–physics–decision framework built around a single 3D, editable surfacerepresentation for vehicle shapes. A vision module turns text and 2D references into diverse, standardized 3Dcandidates and supports image-level edits. A physics module, AeroFormer, is a geometry-guidedTransformer surrogate trained on a large-scale vehicle aerodynamics dataset of roughly 50k CFD simulations; threetask-specific heads predict drag ($C_d$), surface pressure, and velocity fields on shared 3D grids. A decision module encodesregulatory size limits and aesthetic constraints as feasibility tests, uses prototype priors and surrogate sensitivitiesto guide free-form deformation edits, and runs a budget-aware propose–evaluate–refine loop in which only the finaltop-$K$ shapes are confirmed by high-fidelity CFD.In extensive experiments across five common vehicle classes, running only five propose–evaluate–refine iterations per vehiclereduces drag by an average of 2–12\% and cuts high-fidelity CFD calls by 50–80\% compared to baseline workflows, whilepreserving or improving styling quality.