2 papers
physics.plasm-ph2024
High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 Tokamak
Niannian Wu, Zongyu Yang, Rongpeng Li +11
The success of reinforcement learning (RL)-based control in tokamaks, an emerging technique for controlled nuclear fusion with improved flexibility, typically requires substantial…
physics.plasm-ph2024
Real-time equilibrium reconstruction by neural network based on HL-3 tokamak
Guohui Zheng, Songfen Liu, Zongyu Yang +5
A neural network model, EFITNN, has been developed capable of real-time magnetic equilibrium reconstruction based on HL-3 tokamak magnetic measurement signals. The model processes…