In recent decades, deep learning (DL) has become a rapidly growing research direction, redefining the state-of-the-art performances in a wide range of techniques, such as object detection and speech recognition. In the aircraft design, dynamics, and control field, many works hinge on the information-rich data-driven approach, which includes the fusion-based prognostic and health management, the airliner's flight safety monitoring, intelligent sensing, and flight control systems development. While DL provides great potentials to solve these data-driven problems, a systematic review and discussion as to how the DL has been/can be used for these problems are still missing in relation to the rapidly developing and widely used DL techniques. In this article, we aim to address this urgent issue to provide a timely overview of the state-of-the-art for applying DL to the aircraft design, dynamics, and control field. In particular, we briefly introduce five representative DL methods, i.e., deep neural network, deep autoencoder, deep belief network, convolutional neural network, and recurrent neural network. Mathematical definitions for each method are presented, and illustrative applications are also discussed. We then review the existing DL-based works that have appeared in the aircraft design, dynamics, and control field. The review efforts are divided into two major groups, i.e., the own-ship aircraft modeling, wherein the works have been/can be implemented online for the aircraft design/dynamics/control, and other airplanes research works, wherein DL-based schemes provide offline monitoring of the aircraft operation. We then summarize the data sources and DL architectures. Referring to the experiences of DL research works/techniques development in other related fields, future opportunities, challenges, and potential solutions for implementing DL in the aircraft design, dynamics, and control field are also discussed.

中文翻译：

---

飞机设计、动力学和控制中的深度学习：回顾与展望

近几十年来，深度学习 (DL) 已成为快速发展的研究方向，重新定义了对象检测和语音识别等广泛技术中的最先进性能。在飞机设计、动力学和控制领域，许多工作依赖于信息丰富的数据驱动方法，包括基于融合的预测和健康管理、客机飞行安全监控、智能传感和飞行控制系统开发。虽然 DL 为解决这些数据驱动的问题提供了巨大的潜力，但与快速发展和广泛使用的 DL 技术相比，关于 DL 如何/可以如何用于这些问题的系统回顾和讨论仍然缺失。在本文中，我们旨在解决这个紧迫的问题，及时概述将 DL 应用于飞机设计、动力学和控制领域的最新技术。特别地，我们简要介绍了五种具有代表性的 DL 方法，即深度神经网络、深度自动编码器、深度信念网络、卷积神经网络和循环神经网络。介绍了每种方法的数学定义，并讨论了说明性应用。然后，我们回顾了在飞机设计、动力学和控制领域出现的现有基于 DL 的作品。审查工作分为两大类，即本机飞机建模，其中的工作已经/可以在线实施，用于飞机设计/动力学/控制，以及其他飞机研究工作，其中基于 DL 的方案提供飞行器运行的离线监控。然后我们总结了数据源和 DL 架构。参考其他相关领域 DL 研究工作/技术开发的经验，还讨论了在飞机设计、动力学和控制领域实施 DL 的未来机遇、挑战和潜在解决方案。