@inproceedings{0f7346cab03a420c9967d2ba7fdd60f0,
title = "Physics-informed Deep Learning to Solve Electromagnetic Scattering Problems",
abstract = "A physical-informed neural network (PINN) is employed to solve electromagnetic scattering problems which can map the incident field to scattered field directly. Numerical simulations on 2D electromagnetic scattering problems are carried out to validate the performance of PINN.",
keywords = "Electromagnetic Scattering, Machine Learning, Physical-informed Neural Network",
author = "Wang, \{Ji Yuan\} and Yuzhao Li and Xue, \{Bo Wen\} and Pan, \{Xiao Min\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022 ; Conference date: 14-12-2022 Through 17-12-2022",
year = "2022",
doi = "10.1109/CAMA56352.2022.10002575",
language = "English",
series = "IEEE Conference on Antenna Measurements and Applications, CAMA",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2022 IEEE Conference on Antenna Measurements and Applications, CAMA 2022",
}