Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Journal Article (Review;Journal)

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.

Full Text

Duke Authors

Cited Authors

  • Khatib, O; Ren, S; Malof, J; Padilla, WJ

Published Date

  • August 1, 2021

Published In

Volume / Issue

  • 31 / 31

Electronic International Standard Serial Number (EISSN)

  • 1616-3028

International Standard Serial Number (ISSN)

  • 1616-301X

Digital Object Identifier (DOI)

  • 10.1002/adfm.202101748

Citation Source

  • Scopus