2D Eigenmode Analysis Based on Physics Informed Neural Networks Conference

Khan, MR, Zekios, CL, Bhardwaj, S et al. (2023). 2D Eigenmode Analysis Based on Physics Informed Neural Networks . 2015 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION & USNC/URSI NATIONAL RADIO SCIENCE MEETING, 2023-July 1015-1016. 10.1109/USNC-URSI52151.2023.10237743

cited authors

  • Khan, MR; Zekios, CL; Bhardwaj, S; Georgakopoulos, SV

abstract

  • In this work, a novel deep learning-based approach for identifying the modal field distributions of closed waveguides is introduced. Specifically, physics informed neural networks are used to solve the Helmholtz partial differential equation (PDE) with the imposition of the appropriate boundary conditions. Based on our results, the proposed approach is capable of identifying all the eigenmode distributions of the studied waveguides with an error of less than -12 dB when compared with both analytical and full-wave simulation results.

publication date

  • January 1, 2023

International Standard Book Number (ISBN) 13

start page

  • 1015

end page

  • 1016

volume

  • 2023-July