Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks

Sahar Bagherkhani, Jackson Christopher Earls, Franco De Flaviis, Pierre Baldi
4/23/2025
cs.LG

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Abstract

Electromagnetic field reconstruction is crucial in many applications, including antenna diagnostics, electromagnetic interference analysis, and system modeling. This paper presents a deep learning-based approach for Far-Field to Near-Field (FF-NF) transformation using Convolutional Neural Networks (CNNs). The goal is to reconstruct near-field distributions from the far-field data of an antenna without relying on explicit analytical transformations. The CNNs are trained on paired far-field and near-field data and evaluated using mean squared error (MSE). The best model achieves a training error of 0.0199 and a test error of 0.3898. Moreover, visual comparisons between the predicted and true near-field distributions demonstrate the model's effectiveness in capturing complex electromagnetic field behavior, highlighting the potential of deep learning in electromagnetic field reconstruction.

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Cite this paper

@article{bagherkhani2025antenna,
  title  = {Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks},
  author = {Sahar Bagherkhani and Jackson Christopher Earls and Franco De Flaviis and Pierre Baldi},
  year   = {2025},
  eprint = {2504.17065},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.17065v1}
}

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