5 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…
Power-law-anchored residual learning for H-mode energy confinement time in tokamaks: interpolation and parameter-defined extrapolation
Zhaokun Wang, Tianyuan Liu, Jianguo Chen +4
Reliable prediction of the energy confinement time is essential for magnetic-confinement fusion. Conventional power-law scalings provide constrained extrapolation trends but cannot…
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…