6 papers
State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U
Pei Guo, Zhengyuan Chen, Jianguo Chen +17
Accurate feedback control of the plasma current () and centroid position is essential for the stable operation of spherical torus (ST) plasmas. Conventional propor…
Reinforcement learning for vertical position control on the EXL-50U spherical tokamak
Lei Xing, Huicong Ma, Changquan Yu +14
Vertical position control is essential for sustaining high-performance operation in spherical tokamaks, where increased plasma elongation introduces stringent requirements on fast…
Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment
Siqi Ding, Xuanhe Wang, Pei Guo +10
Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation volume from the core, EHL-2 adop…
CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
Qingyong Zhu, Yumin Tan, Xiang Gu +1
Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction. Representative FU-DG…
Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation
Siqi Ding, Zitong Zhang, Guoyang Shi +7
As artificial intelligence emerges as a transformative enabler for fusion energy commercialization, fast and accurate solvers become increasingly critical. In magnetic confinement…
EFIT-mini: An Embedded, Multi-task Neural Network-driven Equilibrium Inversion Algorithm
Guohui Zheng, Songfen Liu, Huasheng Xie +11
Equilibrium reconstruction, which infers internal magnetic fields, plasmas current, and pressure distributions in tokamaks using diagnostic and coil current data, is crucial for co…