This is a preview of subscription content, access via your institution.
References
- 1
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
- 2
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
- 3
Giacché D, Xu L, Coupland J. Optimization of bypass outlet guide vane for low interaction noise. AIAA J, 2014, 52: 1145–1158
- 4
Kutz J N. Deep learning in fluid dynamics. J Fluid Mech, 2017, 814: 1–4
- 5
Zhang W D, Wang Y B, Liu Y. Aerodynamic study of theater ballistic missile target. Aerosp Sci Tech, 2013, 24: 221–225
- 6
Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning. 2015. ArXiv:1509.02971
- 7
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
- 8
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).
Author information
Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published: