Aerodynamic modeling plays an important role in multiphysics and design problems, in addition to experiment and numerical simulation, due to its low-dimensional representation of unsteady aerodynamics. However, in the traditional study of aerodynamics, developing aerodynamic and flow models relies on classical theoretical (potential flow) and empirical investigation, which limits the accuracy and extensibility. Recently, with significant progress in high-fidelity computational fluid dynamic simulation and advanced experimental techniques, very large and diverse fluid data becomes available. This rapid growth of data leads to the development of data-driven aerodynamic and flow modeling. Through advanced mathematical methods from control theory, data science and machine learning, a lot of data-driven aerodynamic models have been proposed. These models are not only more accurate than theoretical models, but also require very low computational cost compared with numerical simulation. At the same time, they help to gain physical insights on flow mechanism, and have shown great potential in engineering applications like flow control, aeroelasticity and optimization. In this review paper, we introduce three typical data-driven methods, including system identification, feature extraction and data fusion. In particular, main approaches to improve the performance of data-driven models in accuracy, stability and generalization capability are reported. The efficacy of data-driven methods in modeling unsteady aerodynamics is described by several benchmark cases in fluid mechanics and aeroelasticity. Finally, future development and potential applications in related areas are concluded.

除了实验和数值模拟之外，空气动力学建模在多物理场和设计问题中发挥着重要作用，因为它对非定常空气动力学进行了低维表示。然而，在传统的空气动力学研究中，开发空气动力学和流动模型依赖于经典理论（势流）和实证研究，这限制了准确性和可扩展性。最近，随着高保真计算流体动力学模拟和先进实验技术的重大进展，非常大和多样化的流体数据变得可用。数据的这种快速增长导致了数据驱动的空气动力学和流动建模的发展。通过控制理论、数据科学和机器学习的先进数学方法，已经提出了许多数据驱动的空气动力学模型。这些模型不仅比理论模型更准确，而且与数值模拟相比，计算成本也非常低。同时，它们有助于获得对流动机制的物理见解，并在流动控制、气动弹性和优化等工程应用中显示出巨大的潜力。在这篇综述论文中，我们介绍了三种典型的数据驱动方法，包括系统识别、特征提取和数据融合。特别是，报告了提高数据驱动模型在准确性、稳定性和泛化能力方面的性能的主要方法。流体力学和气动弹性方面的几个基准案例描述了数据驱动方法在模拟非定常空气动力学方面的功效。最后，总结了相关领域的未来发展和潜在应用。