1. Huang, J. and J. A. Encinar, Reflectarray Antennas, Vol. 30, John Wiley & Sons, 2007.
doi:10.1002/9780470178775

2. Mao, Y., S. Xu, F. Yang, and A. Z. Elsherbeni, "A novel phase synthesis approach for wideband reflectarray design," IEEE Transactions on Antennas and Propagation, Vol. 63, No. 9, 4189-4193, 2015.
doi:10.1109/TAP.2015.2447004        Google Scholar

3. Yang, H., F. Yang, X. Cao, S. Xu, J. Gao, X. Chen, M. Li, and T. Li, "A 1600-element dual-frequency electronically reconfigurable reflectarray at X/Ku-band," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 6, 3024-3032, 2017.
doi:10.1109/TAP.2017.2694703        Google Scholar

4. Barbiere, D., "A method for calculating the current distribution of Tschebyscheff arrays," Proceedings of the IRE, Vol. 40, No. 1, 78-82, 1952.
doi:10.1109/JRPROC.1952.273938        Google Scholar

5. Chakraborty, A., B. Das, and G. Sanyal, "Beam shaping using nonlinear phase distribution in a uniformly spaced array," IEEE Transactions on Antennas and Propagation, Vol. 30, No. 5, 1031-1034, 1982.
doi:10.1109/TAP.1982.1142917        Google Scholar

6. Nayeri, P., F. Yang, and A. Z. Elsherbeni, Reflectarray Antennas: Theory, Designs, and Applications, Wiley-IEEE Press, 2018.
doi:10.1002/9781118846728

7. Johnson, J. M. and Y. Rahmat-Samii, "Genetic algorithm optimization and its application to antenna design," Proceedings of IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, Vol. 1, 326-329, IEEE, 1994.
doi:10.1109/APS.1994.407746        Google Scholar

8. Lommi, A., A. Massa, E. Storti, and A. Trucco, "Sidelobe reduction in sparse linear arrays by genetic algorithms," Microwave and Optical Technology Letters, Vol. 32, No. 3, 194-196, 2002.
doi:10.1002/mop.10128        Google Scholar

9. Robinson, J. and Y. Rahmat-Samii, "Particle swarm optimization in electromagnetics," IEEE Transactions on Antennas and Propagation, Vol. 52, No. 2, 397-407, 2004.
doi:10.1109/TAP.2004.823969        Google Scholar

10. Ferreira, J. A. and F. Ares, "Pattern synthesis of conformal arrays by the simulated annealing technique," Electronics Letters, Vol. 33, No. 14, 1187-1189, 1997.
doi:10.1049/el:19970838        Google Scholar

11. Prado, D. R., A. F. Vaquero, M. Arrebola, M. R. Pino, and F. Las-Heras, "Acceleration of gradient-based algorithms for array antenna synthesis with far-field or near-field constraints," IEEE Transactions on Antennas and Propagation, Vol. 66, No. 10, 5239-5248, 2018.
doi:10.1109/TAP.2018.2859915        Google Scholar

12. Mahanti, G., A. Chakraborty, and S. Das, "Phase-only and amplitude-phase only synthesis of dual-beam pattern linear antenna arrays using oating-point genetic algorithms," Progress In Electromagnetics Research, Vol. 68, 247-259, 2007.
doi:10.2528/PIER06072301        Google Scholar

13. Capozzoli, A., C. Curcio, A. Liseno, and G. Toso, "Fast, phase-only synthesis of aperiodic reflectarrays using NUFFTs and CUDA," Progress In Electromagnetics Research, Vol. 156, 83-103, 2016.
doi:10.2528/PIER16021904        Google Scholar

14. Robustillo, P., J. Zapata, J. A. Encinar, and J. Rubio, "Ann characterization of multi-layer reflectarray elements for contoured-beam space antennas in the Ku-band," IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, 3205-3214, 2012.
doi:10.1109/TAP.2012.2196941        Google Scholar

15. El Zooghby, A. H., C. G. Christodoulou, and M. Georgiopoulos, "A neural network-based smart antenna for multiple source tracking," IEEE Transactions on Antennas and Propagation, Vol. 48, No. 5, 768-776, 2000.
doi:10.1109/8.855496        Google Scholar

16. El Zooghby, A. H., C. G. Christodoulou, and M. Georgiopoulos, "A neural-network-based linearly constrained minimum variance beamformer," Microwave and Optical Technology Letters, Vol. 21, No. 6, 451-455, 1999.
doi:10.1002/(SICI)1098-2760(19990620)21:6<451::AID-MOP15>3.0.CO;2-M        Google Scholar

17. Prado, D. R., J. A. Lopez-Fernandez, G. Barquero, M. Arrebola, and F. Las-Heras, "Fast and accurate modeling of dual-polarized reflectarray unit cells using support vector machines," IEEE Transactions on Antennas and Propagation, Vol. 66, No. 3, 1258-1270, 2018.
doi:10.1109/TAP.2018.2790044        Google Scholar

18. Prado, D. R., J. A. López-Fernández, M. Arrebola, and G. Goussetis, "Support vector regression to accelerate design and crosspolar optimization of shaped-beam reflectarray antennas for space applications," IEEE Transactions on Antennas and Propagation, Vol. 67, No. 3, 1659-1668, 2018.
doi:10.1109/TAP.2018.2889029        Google Scholar

19. Collobert, R. and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning," Proceedings of the 25th International Conference on Machine Learning, 160-167, 2008.        Google Scholar

20. Krizhevsky, A., I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, Vol. 25, 1097-1105, 2012.        Google Scholar

21. Ng, J. Y.-H., M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond short snippets: Deep networks for video classification," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4694-4702, 2015.        Google Scholar

22. Guo, X., W. Li, and F. Iorio, "Convolutional neural networks for steady flow approximation," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 481-490, 2016.
doi:10.1145/2939672.2939738        Google Scholar

23. Massa, A., D. Marcantonio, X. Chen, M. Li, and M. Salucci, "DNNs as applied to electromagnetics, antennas, and propagation - A review," IEEE Antennas and Wireless Propagation Letters, Vol. 18, No. 11, 2225-2229, 2019.
doi:10.1109/LAWP.2019.2916369        Google Scholar

24. Chen, X., Z. Wei, M. Li, and P. Rocca, "A review of deep learning approaches for inverse scattering problems (invited review)," Progress In Electromagnetics Research, Vol. 167, 67-81, 2020.
doi:10.2528/PIER20030705        Google Scholar

25. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4, 1849-1860, 2018.
doi:10.1109/TGRS.2018.2869221        Google Scholar

26. Li, M., R. Guo, K. Zhang, Z. Lin, F. Yang, S. Xu, X. Chen, A. Massa, and A. Abubakar, "Machine learning in electromagnetics with applications to biomedical imaging: A review," IEEE Antennas and Propagation Magazine, 2021.        Google Scholar

27. Shan, T., W. Tang, X. Dang, M. Li, F. Yang, S. Xu, and J. Wu, "Study on a fast solver for poisson's equation based on deep learning technique," IEEE Transactions on Antennas and Propagation, Vol. 68, No. 9, 6725-6733, 2020.
doi:10.1109/TAP.2020.2985172        Google Scholar

28. Shan, T., X. Pan, M. Li, S. Xu, and F. Yang, "Coding programmable metasurfaces based on deep learning techniques," IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 10, No. 1, 114-125, 2020.
doi:10.1109/JETCAS.2020.2972764        Google Scholar

29. Shan, T., M. Li, S. Xu, and F. Yang, "Synthesis of reflectarray based on deep learning technique," 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 1-2, IEEE, 2018.        Google Scholar

30. Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, "Improving neural networks by preventing co-adaptation of feature detectors,", arXiv preprint arXiv:1207.0580, 2012.        Google Scholar

31. Kingma, D. P. and J. Ba, "Adam: A method for stochastic optimization,", arXiv preprint arXiv:1412.6980, 2014.        Google Scholar