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Recently, deep learning algorithms have been introduced to design metasurfaces, but the network can only output one solution for each desired input and suffers from nonunique issue. To overcome the aforementioned challenges, a deep neural network model for inverse designs of anisotropic metasurfaces with full phase properties in ultrawideband is proposed. Given the target reflection spectra as inputs, the candidate metasurface patterns are generated through a generative adversarial network (GAN), and the corresponding predictions are simply achieved by the accurate forward neural network model to match the target spectra in the whole band with high fidelity. By training the generator and discriminator in GAN in an alternating order combined with setting a threshold of discriminator loss to trigger the phase prediction, the proposed method is much more efficient and consumes less time in the training process. Numerical simulations and experimental results demonstrate that the reflection phases of the generated meta\u2010atoms have excellent agreements with the given targets, providing an efficient way in automatically designing metasurfaces. The most important advantage of this approach over the previous schemes is to improve the design speed significantly with very good accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202000068","type":"journal-article","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T01:55:38Z","timestamp":1590717338000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Deep Learning Designs of Anisotropic Metasurfaces in Ultrawideband Based on Generative Adversarial Networks"],"prefix":"10.1002","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-2957","authenticated-orcid":false,"given":"Hai Peng","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Millimeter Waves Southeast University  Nanjing 210096 China"},{"name":"National Mobile Communications Research Laboratory Southeast University  Nanjing 210096 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