References
van Nguyen N, Tyan M, Jin S, et al. Adaptive multifidelity constraints method for efficient multidisciplinary missile design framework. J Spacecr Rockets, 2016, 53: 184–194
Ni A, Zhang Y F, Chen H X. An improvement to NSGA-II algorithm and its application in optimization design of multi-element airfoil. Acta Aerodynamica Sin, 2014, 32: 252–257
Giacché D, Xu L, Coupland J. Optimization of bypass outlet guide vane for low interaction noise. AIAA J, 2014, 52: 1145–1158
Kutz J N. Deep learning in fluid dynamics. J Fluid Mech, 2017, 814: 1–4
Zhang W D, Wang Y B, Liu Y. Aerodynamic study of theater ballistic missile target. Aerosp Sci Tech, 2013, 24: 221–225
Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning. 2015. ArXiv:1509.02971
Oquab M, Bottou L, Laptev I, et al. Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 2014. 1717–1724
Biancolini M E, Costa E, Cella U, et al. Glider fuselage-wing junction optimization using CFD and RBF mesh morphing. Aircraft Eng Aerosp Tech, 2016, 88: 740–752
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 61603210) and Aeronautical Science Foundation of China (Grant No. 20160758001).
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Yan, X., Zhu, J., Kuang, M. et al. Missile aerodynamic design using reinforcement learning and transfer learning. Sci. China Inf. Sci. 61, 119204 (2018). https://doi.org/10.1007/s11432-018-9463-x
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DOI: https://doi.org/10.1007/s11432-018-9463-x