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Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review
Author(s) -
Khatib Omar,
Ren Simiao,
Malof Jordan,
Padilla Willie J.
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202101748
Subject(s) - metamaterial , deep learning , plasmon , electromagnetic field , artificial intelligence , artificial neural network , field (mathematics) , focus (optics) , computer science , key (lock) , nanotechnology , materials science , physics , optics , optoelectronics , mathematics , computer security , quantum mechanics , pure mathematics
Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.