广义微波网络驱动的超表面物理信息神经算子代理模型
Generalized microwave network-driven physics-informed neural operator surrogate model for metasurfaces network modeling
摘要
Abstract
To address the computational bottlenecks in the optimized design of reconfigurable electromagnetic metasurfaces and achieve a transition from full-wave simulations to efficient intelligent operator inference,this paper proposes a metasurface physics-informed neural operator surrogate model driven by generalized microwave network modeling.First,a unified near/far-field physical modeling method based on generalized microwave network theory is proposed,enabling accurate field prediction through scattering parameter matrices,effective excitation,and angular spectrum propagation.Then,a physics-guided Fourier neural operator(FNO)intelligent surrogate framework is constructed to learn the operator mapping from port configurations to field distributions.Preliminary validation based on an 8-focus focusing scenario using a 20×20 PIN diode metasurface shows that the proposed generalized microwave network method aligns well with HFSS full-wave simulations,providing a reliable data foundation for AI training.Under the case studied in this paper,the trained FNO intelligent operator consumes only 0.01％of the peak memory compared to HFSS full-wave simulation,with a near-field prediction error below 5％and a power conservation error less than 10％.Experimental results demonstrate that the generalized microwave network theory enables a learnable operator representation of complex electromagnetic processes through effective excitation and port-field decomposition.The Fourier neural operator trained on this basis achieves efficient approximation of operator mappings via spectral convolutions in the frequency domain,making the proposed framework suitable for real-time optimization scenarios of metasurfaces.关键词
物理引导神经网络/傅里叶神经算子(FNO)/可重构超表面/实时优化/电磁-AI融合Key words
physically guided neural networks/Fourier neural operator(FNO)/reconfigurable metasurface/real-time optimization/electromagnetic-AI fusion分类
信息技术与安全科学引用本文复制引用
郑哲,朱利豪,冯强,薛皓,史琰,李龙..广义微波网络驱动的超表面物理信息神经算子代理模型[J].电波科学学报,2026,41(1):107-116,10.基金项目
国家重点研发计划(2023YFB3811503) （2023YFB3811503）
国家资助博士后研究人员计划C档(GZC20250387) （GZC20250387）
"三秦博创"人才支持项目(2025SQBC016) （2025SQBC016）
国家自然科学基金信息超材料卓越研究群体项目(62288101) （62288101）
中央高校基本科研业务费专项资金(20103224952,XJSJ25004) （20103224952,XJSJ25004）
中国博士后科学基金(2023M742731)National Key Research and Development Program of China(2023YFB3811503) （2023M742731）
Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation(GZC20250387) （Grade C）
Shaanxi Postdoctoral Innovation Talents Support Program(2025SQBC016) （2025SQBC016）
National Natural Science Foundation of China's Outstanding Research Group Project on Information Metamaterials(62288101) （62288101）
Fundamental Research Funds for the Central Universities(20103224952,XJSJ25004) （20103224952,XJSJ25004）
China Postdoctoral Science Foundation(2023M742731) （2023M742731）
