Informed Deep Learning in Metamaterials

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep neural networks have demonstrated capability to solve challenging forward and inverse problems in electromagnetic metamaterials. However, they often require large quantities of data to achieve a given level of accuracy, which poses a data bottleneck issue and an initial delay in progress. Here we demonstrate two informed deep learning approaches which address the data bottleneck issue in metamaterial design. We show that through direct inclusion of physics in deep neural networks the required network size as well as the size of the dataset can be reduced compared to a vanilla feed forward neural network. We classify our informed deep learning approaches using a recently proposed taxonomy and give an outlook of this exciting field.

Original languageEnglish
Title of host publication2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781733509633
DOIs
StatePublished - 2023
Event2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023 - Monterey, United States
Duration: Mar 26 2023Mar 30 2023

Publication series

Name2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

Conference

Conference2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023
Country/TerritoryUnited States
CityMonterey
Period03/26/2303/30/23

Keywords

  • deep learning
  • inverse design
  • metamaterials
  • metasurfaces
  • physics informed

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