@inproceedings{42ee1fe52375478eaac97569b6b750dc,
title = "A Triple-Network Dual-Driven Framework for Plasma Electron Density Inversion",
abstract = "We propose a triple-network, dual-driven deep learning framework for plasma electron density inversion. It includes the Antenna Decoupling Network (ADNet), which maps antenna reflection coefficients to plane-wave equivalents; the Initial Estimation Network (IENet), which provides coarse electron density predictions; and the Data-Enhanced Physics-Informed Neural Network (DE-PINN), which performs inversion with Maxwell-based physical constraints. By combining data-driven initialization and physics-informed modeling, the framework enhances both accuracy and generalization. Numerical results validate its effectiveness in reconstructing electron density profiles for non-uniform plasmas.",
keywords = "Plasma diagnostics, data-physics-driven learning, inversion, multi-network framework, neural network",
author = "Xin, \{Xu Dong\} and Liu, \{Jin Gang\} and Huang, \{Xiao Wei\} and Sheng, \{Xin Qing\}",
note = "Publisher Copyright: {\textcopyright} 2025 Applied Computational Electromagnetics Society.; 2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 ; Conference date: 08-08-2025 Through 11-08-2025",
year = "2025",
doi = "10.23919/ACES-China66523.2025.11333194",
language = "English",
series = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 International Applied Computational Electromagnetics Society Symposium, ACES-China 2025 - Proceedings",
address = "United States",
}