Aerodynamic inverse design can improve vehicle and aircraft efficiency, but practical design rarely seeks performance alone: vehicle refinement must reduce drag while preserving visual features linked to design language, brand recognition and user perception. Traditional CFD-driven optimization is accurate but slow for broad exploration, and current learning-based methods are still largely performance-driven and lack a coherent target linking optimization, generation and visual consistency. Here we formulate visual preservation and aerodynamic improvement as one probability target. Designs consistent with a reference shape or view define a learned visual design distribution, which is reweighted by aerodynamic cost. Optimization then refines an initial geometry toward a low-cost, high-probability design, whereas guided generation samples lower-cost 3D candidates from the same input view. OpenFOAM evaluation shows that visual-feature-preserving optimization reduces vehicle drag by 5.8\% relative to the initial vehicle and reduces the best valid aircraft drag-to-lift objective by 28.8\% relative to the initial aircraft while preserving input visual features. For view-based generation, guidance reduces vehicle drag by 3.0\% and the aircraft drag-to-lift objective by 68.6\% relative to direct generation from the same view, while maintaining visual consistency. Wind-tunnel tests with 3D-printed vehicle prototypes provide an independent wake-level check, and controlled analyses explain the distributional mechanisms behind these results. This work provides a probabilistic foundation and practical route for visual-feature-preserving aerodynamic refinement and early-stage 3D design exploration.
Reinforcement Learning for Accelerated Aerodynamic Shape Optimisation
Software Competence Center Hagenberg (SCCH)（软件竞争力中心哈根贝格）；Institut für Strömungsmechanik und Wärmeübertragung, TU Wien（流体力学与传热研究所，维也纳技术大学）；CERBSim GmbH（CERBSim公司）
Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models
University of Kaiserslautern-Landau (RPTU)（凯泽斯劳滕-兰道大学（RPTU））；Institute for Machine Learning, ETH Zürich（苏黎世联邦理工学院机器学习研究所）；Department of Management Science and Engineering and ICME, Stanford University（斯坦福大学管理科学与工程系及ICME部门）
TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks
Department of concepts and methods development in virtual fields, AUDI AG（虚拟领域概念与方法发展系，奥迪公司）；Technology and Bionics Faculty, Rhine-Waal University of Applied Sciences（技术与生物科学系，莱茵-瓦尔大学）；Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；Schwarzman College of Computing, Massachusetts Institute of Technology（施瓦茨曼计算学院，麻省理工学院）
A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics
DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimization
NVIDIA；Baidu, Inc.（百度公司）；Peking University（北京大学）；State Key Lab of General AI, Peking University（北京大学通用人工智能国家重点实验室）；Beijing Key Laboratory of Behavior and Mental Health, Peking University（北京市行为与心理健康重点实验室）；Embodied Intelligence Lab, PKU-Wuhan Institute for Artificial Intelligence（embodied intelligence lab, PKU-Wuhan人工智能研究所）
Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction
Université Toulouse Capitole（图卢兹第一大学）；Toulouse INP（图卢兹国立综合理工学院）；Université de Toulouse（图卢兹大学）；Imperial College London（伦敦帝国学院）；ISAE-SUPAERO（法国高等航空航天学院）；MINES ALBI（阿尔比矿业学校）；UPS（图卢兹第三大学）；INSA（国立应用科学学院）；CNRS（法国国家科学研究中心）
AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院机械工程系）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院电气工程与计算机科学系）；Department of Computer Science, Technical University of Munich, Munich, Germany（慕尼黑技术大学计算机科学系）
Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
School of Ocean and Civil Engineering, Shanghai Jiao Tong University（上海交通大学海洋与土木工程学院）；Eastern Institute of Technology（东方理工学院）；TenFong Technology Co., Ltd.（十方科技有限公司）；IM Motors Technology Co., Ltd.（集度汽车科技有限公司）
