@inproceedings{eb2904fdbe0c4754a9990095d52d4b1e,
title = "SE-CoordFieldNet: A Coordinate-Aware Neural Network for Predicting Near-Field of Antennas",
abstract = "This paper presents SE-CoordFieldNet, a coordinate-aware convolutional neural network designed to predict near-field electric field distributions under diverse antenna configurations. The model integrates spatial coordinate encoding and attention mechanisms to enhance feature representation and improve prediction accuracy. It supports multi-frequency, multi-structure antenna scenarios, offering efficient assistance for antenna design and electromagnetic compatibility evaluation. Compared with traditional fully connected networks and full-wave simulations, the proposed model achieves moderately better accuracy and computational efficiency.",
keywords = "Antenna Near-Field Prediction, Convolutional neural network(CNN), CoordConv, EMC, Squeeze-and-excitation",
author = "Siqi Lu and Cheng Cao and Zhiliang Zhong and Yaoyao Li",
note = "Publisher Copyright: {\textcopyright} 2025 Applied Computational Electromagnetics Society.; 2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 ; Conference date: 08-08-2025 Through 11-08-2025",
year = "2025",
doi = "10.23919/ACES-China66523.2025.11332944",
language = "英语",
series = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
address = "美国",
}