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

This 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 in...

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Bibliographic Details
Published in:Microwave and optical technology letters 2024-07, Vol.66 (7), p.n/a
Main Authors: Park, Jae‐Yoon, Kim, Yong‐Hwa, Cho, Chihyun, Choo, Jaeyul
Format: Article
Language:English
Subjects:
Artificial neural networks
Datasets
deep neural network
Dipoles
Electromagnetic radiation
Far fields
infinitesimal dipole modeling
Modelling
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Summary:This 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.
ISSN:0895-2477
1098-2760
DOI:10.1002/mop.34252