Neural-network based electron density profile inversion for interferometer on EAST tokamak

作者：Xiaoping Xie, Ting Lan, Haiqing Liu, Xiang Zhu, Wenzhe Mao, Tao Lan, Weixing Ding · 发表于：Plasma Physics and Controlled Fusion · 年份：2025 · DOI：10.1088/1361-6587/adba12 · 被引用次数：7 · 研究领域：Magnetic confinement fusion research、Advanced Electrical Measurement Techniques

Abstract The Back Propagation Neural Network (BPNN) has been applied to the density inversion problem of the POlarimeter INTerferometer (POINT) system on the EAST tokamak. Using the BPNN, the electron density profile can be directly reconstructed from the line-integrated density measurement provided by the POINT system. The accuracy and reliability of this approach have been investigated through tests on experimental data. Compared to the traditional Park-matrix method, the BPNN-based model demonstrates significantly faster performance and greater robustness against system noise, making it suitable for real-time control of the density profile. Additionally, the influence of various measurement channels on the inverted density profile has been thoroughly analyzed, offering a quantitative approach to optimizing interferometer design for future machines.