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

arXiv: https://arxiv.org/abs/2504.17065 · HTML: https://mlsanity.com/paper/2504.17065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Sherpa: Robust Hyperparameter Optimization for Machine Learning](https://mlsanity.com/paper/2005.04048/) (2020)

## Abstract (arXiv)

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.