A Deep-Learning Characteristic Modes Classification Model for Patch Antennas Conference

Sendrea, RE, Zekios, CL, Georgakopoulos, SV. (2022). A Deep-Learning Characteristic Modes Classification Model for Patch Antennas . 491-492. 10.1109/AP-S/USNC-URSI47032.2022.9886407

cited authors

  • Sendrea, RE; Zekios, CL; Georgakopoulos, SV

abstract

  • In this work, a deep-learning surrogate model is developed with the objective to predict the characteristic modes of any general geometry. Namely, a physics-based correlation function is introduced capable of classifying the modal currents of different geometries. These currents are used in turn to train our image-based supervised deep neural network. To generate the desired number of arbitrary geometrical shapes, Gielis' supershape formula is utilized. As a proof-of-concept, our algorithm is tested on patch antennas that have shapes of perturbed rectangles. Our results show that our deep neural network has good predictive ability.

publication date

  • January 1, 2022

International Standard Book Number (ISBN) 13

start page

  • 491

end page

  • 492