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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.17065 |
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| _version_ | 1866909591294443520 |
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| author | Bagherkhani, Sahar Earls, Jackson Christopher De Flaviis, Franco Baldi, Pierre |
| author_facet | Bagherkhani, Sahar Earls, Jackson Christopher De Flaviis, Franco Baldi, Pierre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks Bagherkhani, Sahar Earls, Jackson Christopher De Flaviis, Franco Baldi, Pierre Machine Learning 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. |
| title | Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.17065 |