电波科学学报2026，Vol.41Issue(1)：53-64,12.DOI:10.12265/j.cjors.2025174
融合物理信息的双分支神经网络的三维介质体电磁散射参数化模型
A parameterized model of electromagnetic scattering from three-dimensional dielectric objects based on a dual-branch neural network integrating physical information
摘要
Abstract
With the widespread use of low-observable dielectric objects such as drones,composite structures,and radomes in engineering,the demand for electromagnetic scattering modeling for these targets is rapidly increasing.However,due to the multiple reflection and transmission effects of electromagnetic waves within dielectric objects,traditional numerical methods typically require significant time and computing resources for high-precision calculations,making them difficult to meet real-time requirements.To address this issue,this paper proposes a dual-branch neural network that integrates physical information to construct a parameterized electromagnetic scattering model for three-dimensional dielectric targets.By incorporating geometric prior information and phase modulation,combined with a loss function designed based on amplitude-phase consistency,this method achieves efficient learning of scattering characteristics while ensuring physical consistency.This model enables rapid prediction of target radar cross sections at the decibel scale.To validate the effectiveness of this method,numerical experiments were conducted on a commercial NVIDIA GPU platform for targets with various geometric shapes and dielectric parameters,including a frustum model and a fixed-wing drone.Results demonstrate that the proposed model maintains prediction accuracy consistent with full-wave simulations while achieving millisecond-scale inference times,demonstrating its potential for real-time application in complex electromagnetic environments.关键词
深度学习/卷积神经网络/介质材料/电磁散射/雷达散射截面Key words
deep learning/convolutional neural networks/dielectric materials/electromagnetic scattering/radar cross section分类
信息技术与安全科学引用本文复制引用
李则霖,吴比翼,郭琨毅,盛新庆..融合物理信息的双分支神经网络的三维介质体电磁散射参数化模型[J].电波科学学报,2026,41(1):53-64,12.基金项目
国家重点研发计划(2023YFB3002600) （2023YFB3002600）
国家自然科学基金(62231003,62271046)National Key Research and Development Program of China(2023YFB3002600) （62231003,62271046）
National Natural Science Foundation of China(62231003,62271046) （62231003,62271046）
