Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems.
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）
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
School of Aerospace Engineering, Tsinghua University, Beijing, China（清华大学北京航空航天工程学院）；School of Computation, Information and Technology, Technical University of Munich, Germany（慕尼黑技术大学计算、信息与技术学院）
Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
Department of Aerospace Engineering, Tohoku University（东京东京大学航空工程系）；Department of Mechanical and Aerospace Engineering, Tokyo University of Science（东京科学大学机械与航空航天工程系）
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）
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）
DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
Massachusetts Institute of Technology（麻省理工学院）；Technical University of Munich（慕尼黑技术大学）；Department of Mechanical Engineering（机械工程系）；Department of Computer Science（计算机科学系）
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）
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）
Improving aircraft performance using machine learning: a review
Universidad Politécnica de Madrid（马德里理工大学）；University of Sheffield（谢菲尔德大学）；Université Libre de Bruxelles（布鲁塞尔自由大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Shape-morphing programming of soft materials on complex geometries via neural operator
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学机械与电子工程学院）；Université Sorbonne Paris Nord（巴黎 Sorbonne 大学）；School of Mechanical and Power Engineering, Nanjing Tech University（南京理工大学机械与 power 工程学院）；Department of Production Engineering, KTH Royal Institute of Technology（皇家理工学院生产工程系）
