Deep neural network‐based infinitesimal dipole modeling using either near or far electric‐field

Abstract

AbstractThis paper presents the deep neural network‐based infinitesimal dipole model using either near‐ or far‐field radiation patterns. Based on the radiating characteristic of an infinitesimal dipole, we generated a data set including near and far field radiation patterns corresponding to the phases of infinitesimal dipoles. We used the data set to train and validate the deep neural network (DNN) model. After checking the statistic of the fit function and the average error in expecting the phases of the infinite dipoles, we conclude that the proposed infinitesimal dipole modeling using DNN is sufficient to predict the characteristics of electromagnetic radiation.