@inproceedings{f6757bc980e64df59f55cc4345b04eb0,
title = "Physics-Informed Neural Networks with Hard Constraints for Electromagnetic Scattering Analysis",
abstract = "In this paper, we present a novel approach to solving two-dimensional electromagnetic scattering problem using physical-informed neural networks (PINN) enhanced with hard constraints. The proposed method is implemented under the partial differential equations (PDEs) framework, which demonstrates superior accuracy over traditional PINN structure compared to conventional numerical methods.",
keywords = "Electromagnetic Scattering, Hard Constraints, Physical-informed Neural Networks",
author = "Hang Li and Liu, {Jin Gang} and Huang, {Xiao Wei} and Sheng, {Xin Qing}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 14th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2024 ; Conference date: 23-10-2024 Through 26-10-2024",
year = "2024",
doi = "10.1109/ISAPE62431.2024.10841206",
language = "English",
series = "ISAPE 2024 - 14th International Symposium on Antennas, Propagation and EM Theory",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISAPE 2024 - 14th International Symposium on Antennas, Propagation and EM Theory",
address = "United States",
}