用CNN从远场数据重建天线近场，无需解析公式。
Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks
- 用卷积神经网络学习远场到近场的映射关系。
- 测试误差达0.3898，能有效还原复杂电磁场分布。
- 适合天线诊断与电磁干扰分析场景。
电磁场重构在天线诊断、电磁干扰分析和系统建模中至关重要。本文提出一种基于深度学习的远场到近场（FF-NF）转换方法，利用卷积神经网络（CNN）从天线远场数据重构近场分布，无需依赖显式解析变换。CNN 在配对的远场与近场数据上进行训练，并以均方误差（MSE）评估性能。最佳模型在训练集上误差为0.0199，在测试集上误差为0.3898。视觉对比显示，预测近场分布与真实分布高度一致，表明该方法能有效捕捉复杂的电磁场行为，展示了深度学习在电磁场重构中的潜力。
原文摘要 · Abstract (English)
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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