@inproceedings{4bd1ca358bcb4f918aaff0cc23032dd3,
title = "A Deep-Learning-Assisted Integral Equation Solver for Cost-Effective em Analysis of 3-D PEC Targets",
abstract = "Surface integral equation solvers are an attractive approach for analyzing scattering problems involving metallic targets. However, when applied to large-scale targets, they still face the challenge of high memory and computational resource consumption. In this work, we propose a deep-learning-assisted combined field integral equation solver (DL-CFIE) for cost-effective electromagnetic (EM) analysis of PEC targets. In this method, a deep-learning-based neural network (DNN) is designed and trained to replace the traditional preconditioners within the standard CFIE solvers. Numerical experiments are conducted to show the performance of the DL-CFIE, which show that the DL-CFIE solver achieves the same accuracy and convergence as standard CFIE solver while reducing the memory cost significantly.",
keywords = "combined field integral equation, deep-learning, electromagnetic analysis, surface integral equation solvers",
author = "Liang, \{Zi Yang\} and Li, \{Jing Yi\} and Gao, \{Hong Wei\} and Yanjun Zhang",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2026 ; Conference date: 10-07-2026 Through 12-07-2026",
year = "2026",
doi = "10.1109/CSRSWTC71154.2026.11669349",
language = "English",
series = "CSRSWTC 2026 - 2026 Cross Strait Radio Science and Wireless Technology Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "CSRSWTC 2026 - 2026 Cross Strait Radio Science and Wireless Technology Conference, Proceedings",
address = "United States",
}