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
. 2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING, 2023-July 1015-1016. 10.1109/USNC-URSI52151.2023.10237743
Khan, MR, Zekios, CL, Bhardwaj, S et al. (2023). 2D Eigenmode Analysis Based on Physics Informed Neural Networks
. 2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING, 2023-July 1015-1016. 10.1109/USNC-URSI52151.2023.10237743
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.
