[1]王振国, 梁剑寒, 范晓樯, 等.吸气式高速飞行器一体化方案:回顾与展望[J].空气动力学学报, 2023, 41(8):13-25[2]WANG Z G, LIANG J H, FAN X Q, et al.Integrated scheme of air-breathing high-speed aircraft: review and prospect[J].Acta Aerodynamica Sinica, 2023, 41(8):13-25[3]郑晓刚,林德寿,方啸雷,等.基于局部偏转吻切方法的背部进气高超飞行器一体化设计研究[J].空天技术, 2023, 455(5):1-10[4]ZHENG X G, LIN D S, FANG X L, et al.Research on the integration design of hypersonic vehicles with dorsal inlets based on the local-turning osculating cones meth-od[J].Aerospace Technology, 2023, 455(5):1-10[5]陈树生,冯聪,张兆康,等.基于全局梯度优化方法的宽速域乘波-机翼布局气动设计[J].航空学报, 2024, 45(6):629596-629596[6]CHENG S S, FENG C, ZHANG Z K, et al.Global&Local Optimization Design for wide-speed-range wa-verider-wing configuration[J].Acta Aeronautica et As-tronautica Sinica, 2024, 45(6):629596-629596[7]侯强.固体火箭超燃冲压发动机进排气系统设计及流场预测研究[D]. 南京: 南京航空航天大学, 2020: 1-101.[8]HOU Q.Design and Flow Field of Inlet and Nozzle System for Solid Rocket Scramjet[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020: 1-101(in Chinese).[9]杨超,赵黄达,吴志刚.吸气式高超声速飞行器热气动弹性研究进展[J].北京航空航天大学学报, 2019, 45(10):1911-1923[10]YANG C, ZHAO H D,WU Z G.Research pro-gress of aerothermoelasticity of air-breathing hypersonic vehicles[J].Journal of Beijing University of Aero-nautics and Astronautics, 2019, 45(10):1911-1923[11]郭建国, 梁乐成, 周敏, 等.高速飞行器俯冲段制导控制一体化综述[J].航空兵器, 2023, 30(1):1-10[12]GUO J G, LIANG L C, ZHOU M, et al.Overview of integrated guidance and control for hypersonic vehicles in dive phase[J].Aero Weaponry, 2023, 30(1):1-10[13]张文敏, 王剑颖.基于智能图搜索的滑翔式高超声速飞行器路径规划方法[J].飞控与探测, 2022, 5(6):80-92[14]ZHANG W, WANG J.Trajectory planning of hyperson-ic gliding vehicle based on smart graph search method[J].Flight Control & Detection, 2022, 5(6):80-92[15]姜志杰, 韩伟, 李怡庆, 等.基于遗传算法的高超声速飞行器多学科优化设计[J].南昌航空大学学报: 自然科学版, 2022, 36(2):18-23[16]JIANG Z J, HAN W, LI Y Q, et al.Genetic Algorithms Based Multidisciplinary Design Optimization of Hyper-sonic Vehicle Concept Design[J].Journal of Nanchang Hangkong University: Natural Sciences, 2022, 36(2):18-23[17]杨帆,林明月,胡宗民.基于机器学习的高速飞行器双曲率前缘气动热预测方法[18]YANG F, LIN M Y, HU Z M, et al.Fast prediction method of aero-heating of bi-curvature leading edge based on the machine learning[J]. Journal of Beijing University of Aeronautics and Astronautics. https://doi.org/10.13700/j.bh.1001-5965.2022.0746 (in Chinese).[19]刘紫寒, 武鹏飞, 李响, 等.基于Social-LSTM和在线学习的复杂飞行器仿真模型智能校正方法[C]//第三十四届中国仿真大会暨第二十一届亚洲仿真会议论文集. 长沙: 中国仿真学会, 2022.[20]LIU Z H, WU P F, LI X, et al.Intelligent Correction Method for Complex Aircraft Simulation Model based on Social-LSTM and Online Learning[C]// Proceedings of the 34th China Simulation Conference and the 21st Asian Simulation Conference, Changsha: China Simula-tion Institute, 2022(in Chinese).[21]王煜林, 基于神经网络及其可解释原理的高超声速 飞行器执行机构故障诊断方法研究[D].上海: 上海交通大学, 2022: 1-100.[22]WANG Y L.Research on fault diagnosis method of hy-personic aircraft actuator based on neural network and ITS explainable principles[D]. Shanghai: Shanghai Jiao Tong University, 2022: 1-10(in Chinese).[23]GKIMISIS L, DIAS, B, SCOGGINS, JB, et al.Data-Driven Modeling of Hypersonic Reentry Flow with Heat and Mass Transfer[J].AIAA Journal, 2023, 61(8):3269-3286[24]ZHANG T X, CHEN J Q, ZENG F Z, et al.Improve-ment of transition prediction model in hypersonic bound-ary layer based on field inversion and machine learning framework[J].Physics of Fluids, 2023, 35(2):024104-024104[25]Dreyer E R, Grier B J, McNamara J J, et al.Rapid Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced Fidelity Models[J].Journal of Aircraft, 2022, 58(3):663-676[26]SHEN Y, HUANG W, WANG Z G, et al.A deep learn-ing framework for aerodynamic pressure prediction on general three-dimensional configurations[J].Physics of Fluids, 2023, 35(10):107111-107111[27]LIANG S, XU B, ZHANG Y M.Robust Self-Learning Fault-Tolerant Control for Hypersonic Flight Vehicle Based on ADHDP[J].IEEE Transactions on System Man Cybernetics-Systems, 2023, 53(9):5295-5306[28]YAN T, JIANG Z J, LI T, et al.Intelligent maneuver strategy for hypersonic vehicles in three-player pursuit-evasion games via deep reinforcement learning[J].Fron-ties in Neuroscience, 2024, 18(1):1362303-1362303[29]LI X, JI Y H, SONG Y, et al.Modified deep determinis-tic policy gradient based on active disturbance rejection control for hypersonic vehicles[J].Neural Computing & Applications, 2023, 36(8):4071-4081[30]SAMUEL H R, STEVEN L B, JOSHUA L P, et al.Data-driven discovery of partial differential equa-tions[J].Science Advances, 2017, 3(4):1602614-1602614[31]YOHAI B, STEPHAN H, JASON H, et al.Learning data-driven discretizations for partial differential equa-tions[J].The Proceedings of the National Academy of Sciences (PNAS), 2019, 116(31):15344-15349[32]RAISSI M, PERDISKARIS P, KARNIADAKIS G E.Physics-informed neural networks: a deep learning framework for solving forward and inverse problems in-volving nonlinear partial differential equations[J].Jour-nal of Computational Physics, 2019, 378(1):686-707[33]RAMUHALLI P, UDPA L, UDPA S S.Finite-element neural networks for solving differential equations[J].IEEE Transactions on Neural Networks, 2005, 16(6):1381-1392[34]ZHANG R, MENG Q, MA Z M.Deciphering and inte-grating invariants for neural operator learning with vari-ous physical mechanisms[J].National Science Review, 2024, 11(4):1-12[35]王嘉钰,颜力,安凯,等.高超声速飞行器不确定性多学科设计优化综述[J].空天技术, 2022, (6):1-11[36]WANG J Y, YAN L, AN K, et al.Review of uncertainty-based multidisciplinary design optimization for hyper-sonic vehicles[J].Aerospace Technology, 2022, (6):1-11[37]LAI Z Q, WEI K X, FU Y, et al.Prox: differential proximal algorithm modeling for large-scale optimiza-tion[J].ACM Transactions on Graphics, 2023, 42(4):39-39[38]BREVAULT L, BALESDENT M, MORIO J.航空航天系统不确定分析与优化[M]. 岳程斐, 吴凡, 沈强, 李烁, 译. 北京: 国防工业出版社. 2023.[39]BREVAULT L, BALESDENT M, MORIO J.Aero-space system analysis and optimization in uncertainty[J]. YUE C F, WU F, SHEN W, LI S, translated. Beijing: National Defense Industry Press, 2023.[40]陈小前, 姚雯, 赵勇.飞行器多学科设计优化理论与应用研究[M]. 北京: 国防工业出版社. 2023.[41]CHEN X Q, YAO W, ZHAO Y.Multidisciplinary de-sign optimization of flight vehicles theory and applica-tions[M]. Beijing: National Defense Industry Press, 2023. |