Fitting EM Scattering of 3D Rough Surface using Deep Neural Networks

Machine Intelligence in Metamaterials Design

“…Carlo sequences [131] are a form of multiple optimiza-tions, which is also noted in [132] tion as a route to circumventing high wave number problems. Deep neural networks as used to model all-dielectric metasurfaces can also be trained to predict performance at the atomic scale [144], which the researchers validated by comparing direct results against full-wave simulations using a commercial electromagnetic simulator. Magnusson and co-workers [143] use deep neural networks focused on improving the functionality of optical devices.…”

“…With time, it will be integrated into all levels of technology readiness, becoming commonplace as an assistive tool in the computational design of metamaterials. Weng, Ding, Hu, et al [266] 2020 Deep Learning Classification 10 Melo Filho, Angeli, Ophem, et al [267] 2020 ANN Inverse Design 11 Chen, Lu, Karniadakis, et al [268] 2020 Deep Learning Inverse Design 12 Kollmann, Abueidda, Koric, et al [269] 2020 Deep Learning Optimization Framework 13 Qu, Zhu, Shen, et al [270] 2020 ANN Optimization Framework 14 Lai, Amirkulova, and Gerstoft [271] 2021 CNN, GAN Inverse Design 15 Gurbuz, Kronowetter, Dietz, et al [272] 2021 GAN Inverse Design 16 Amirkulova, Tran, and Khatami [273] 2021 Deep Learning Inverse Design 17 Wu, Liu, Jahanshahi, et al [274] 2021 ANN Inverse Design 18 Shah, Zhuo, Lai, et al [275] 2021 RL Optimization Framework 19 Tran, Amirkulova, and Khatami [276] 2022 ANN Inverse Design 20 Wiest, Seepersad, and Haberman [277] 2022 GNN Inverse Design 21 Amirkulova, Zhou, Abbas, et al [278] 2022 Deep Learning Inverse Design 22 Tran, Khatami, and Amirkulova [279] 2022 CNN Inverse Design 23 Li, Chen, Li, et al [280] 2023 CNN Inverse Design 24 Li, Chen, Li, et al [281] 2023 Deep Learning Inverse Design 25 Wang, Chen, Xu, et al [282] 2023 ANN Inverse Design Application field: Electromagnetics 26 Jiang, Xiao, Liu, et al [283] 2010 ANN and scaled conjugate Surrogate model gradient 27 Freitas, Rêgo, and Vasconcelos [140] 2011 ANN Surrogate model 28 Vasconcelos, Rêgo, and Cruz [139] 2012 ANN Surrogate model 29 Sarmah, Sarma, and Baruah [172] 2015 ANN optimization framework 30 Saha and Maity [138] 2016 ANN Surrogate model 31 Nanda, Sahu, and Mishra [108] 2019 ANN Inverse design 32 An, Fowler, Shalaginov, et al [144] 2019 ANN Surrogate model 33 Yuze, Hai, and Qinglin [157] 2019 CNN Classification and clustering 34 Liu, Zhang, and Cui [156] 2019 CNN optimization framework 35 Hodge, Mishra, and Zaghloul [284] 2019 DC-GAN Inverse design 36 Hodge, Mishra, and Zaghloul…”