Jianghang gu — accepted AI-conference papers

2026

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

Ye Liu, Shouyi Liu, Huiyu Yang, Jianghang Gu, Wenhao Fan, Zhongxin Yang +6

CVPR 2026

Modern generative models can propose striking 3D vehicle shapes from text and images, but turning these sketches into aerodynamically efficient, regulation compliant designs still requires weeks of high-fidelity computational fluid dynamics (CFD) and manual iteration. As a result, fast 3D generation

Cited by 0 Source ↗Scholar ↗

2026

UniDrag: Unified Multi-Field Prediction and Robust Shape Optimization for Vehicle Aerodynamics

Ye Liu, Shouyi Liu, Ding Wang, Huiyu Yang, Ruizhe DENG, Qian Li +6

ICML 2026poster

High-fidelity vehicle aerodynamics analysis is bottlenecked by costly CFD simulations. Neural surrogates accelerate prediction but lack inverse design capabilities, while existing generative optimization methods suffer from unstable convergence and frequent engineering constraint violations. We pres…

Cited by 0 Source ↗Scholar ↗