Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

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

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Article number2101748
JournalAdvanced Functional Materials
Volume31
Issue number31
DOIs
StatePublished - Aug 2 2021

Keywords

  • deep learning
  • metamaterials
  • neural networks

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