Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

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 r...

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Bibliographic Details
Published in:Advanced functional materials 2021-08, Vol.31 (31), p.n/a
Main Authors: Khatib, Omar, Ren, Simiao, Malof, Jordan, Padilla, Willie J.
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Electromagnetic properties
Machine learning
Materials science
Metamaterials
neural networks
Photonic crystals
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Summary: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. Deep learning is rapidly transforming traditional research methods and driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are fields where deep learning has tremendous potential. This comprehensive review article presents deep learning techniques for forward and inverse design of AEMs, with a focus on recent advances, key limitations, and future directions.
ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202101748