生成式模型赋能飞行器技术应用研究进展与展望

doi:10.7527/S1000-6893.2024.31194

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生成式模型赋能飞行器技术应用研究进展与展望

陈树生¹,贾苜梁¹,林家豪¹,金世轶¹,高正红¹,王岳青²,马志强¹,李铮³,段辰龙⁴,李佳伟⁵

1. 西北工业大学
2. 中国空气动力研究与发展中心
3. 中国运载火箭技术研究院空间物理实验室
4. 中国航空研究院
5. 沈阳飞机设计研究所扬州协同创新研究院有限公司

收稿日期:2024-09-12 修回日期:2024-11-13 出版日期:2024-11-18 发布日期:2024-11-18
通讯作者: 陈树生
基金资助:
国家自然科学基金重大研究计划培育项目

Research progress and prospects of empowering aircraft technology applications with generative models

Received:2024-09-12 Revised:2024-11-13 Online:2024-11-18 Published:2024-11-18
Contact: Shu-Sheng CHEN

摘要/Abstract

摘要： 在自然语言处理和计算机视觉领域取得颠覆性应用的生成式模型正成为数智化的新型技术基座，是未来驱动飞行器技术智能化发展的重要引擎。本文综述了生成式模型赋能飞行器技术应用进展情况。首先，总结了生成式模型架构的发展历程，详细介绍了变分自编码器、生成对抗网络、扩散模型、Transformer等基本原理架构和改进方向。其次，归纳了生成式模型在飞行器空气动力学、航迹预测和目标检测等领域的典型应用和变革情况；关注了参数化建模、气动预测模型、反设计等飞行器空气动力设计关键技术的发展趋势；探讨了实时航迹预测、完整航迹预测、协同航迹预测和预测误差补偿的智能实现方法；从现有目标检测方法改进角度分析了生成式模型在多尺度融合、超分辨率增强和数据增强中的作用。最后，从模型方法和应用场景拓展角度展望了生成式模型赋能智能飞行器技术未来的研究方向，针对构建可解释的通用大模型和推动垂直领域应用等方面提出了发展建议。

关键词: 生成式人工智能, 飞行器技术, 空气动力设计, 航迹预测, 目标检测

Abstract: Generative models, which have achieved disruptive applications in the fields of natural language processing and computer vision, are becoming the cornerstone of digital intelligence technologies and are served as a crucial engine driving the future development of intelligent aircraft technology. This paper reviews the application progress of aircraft technology empowered with generative models. Firstly, the development history of generative model architectures is summarized. Detailed introductions to the fundamen-tal principles and improvement directions of variational autoencoders, generative adversarial networks, diffusion models, and Transformers are provided. Secondly, it generalizes the typical applications and transformative impacts of generative models in aircraft aerodynamics, trajectory prediction, and target detection. Development trends in key technologies of aircraft aerodynamic design, including parameterized modeling, aerodynamic prediction model and inverse design, are focused. The intelligent imple-mentation methods of real-time trajectory prediction, complete trajectory prediction, collaborative trajectory prediction and pre-diction error compensation are studied. From the perspective of improving existing target detection methods, the roles of genera-tive models in multi-scale fusion, super-resolution enhancement and data enhancement are analyzed. Finally, this paper proposes the future research directions for intelligent aircraft technology empowered with generative models are proposed, from the per-spectives of model method and application scenario expansion. Development suggestions are proposed for building interpretable general models and promoting vertical domain applications.

Key words: Generative artificial intelligence, Aircraft technology, Aerodynamic design, Trajectory prediction, Object detection

中图分类号:

V11
TP183

陈树生贾苜梁林家豪金世轶高正红王岳青马志强李铮段辰龙李佳伟. 生成式模型赋能飞行器技术应用研究进展与展望[J]. 航空学报, doi: 10.7527/S1000-6893.2024.31194.

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生成式模型赋能飞行器技术应用研究进展与展望

Research progress and prospects of empowering aircraft technology applications with generative models

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