Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Khatib, Omar; Ren, Simiao; Malof, Jordan; Padilla, Willie J.

doi:10.1002/adfm.202101748

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Journal Article · Fri May 28 00:00:00 EDT 2021 · Advanced Functional Materials

DOI:https://doi.org/10.1002/adfm.202101748· OSTI ID:1831824

Khatib, Omar ^[1]; Ren, Simiao ^[2]; Malof, Jordan ^[2]; ^[2]

Duke University, Durham, NC (United States); Duke University
Duke University, Durham, NC (United States)

Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Duke University, Durham, NC (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0014372

OSTI ID:: 1831824

Alternate ID(s):: OSTI ID: 1788135

Journal Information:: Advanced Functional Materials, Journal Name: Advanced Functional Materials Journal Issue: 31 Vol. 31; ISSN 1616-301X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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