5 papers
Physics-Gated Visual Prediction of MARFE on the HL-3 Tokamak
Qianyun Dong, Rongpeng Li, Zongyu Yang +5
The Multifaceted Asymmetric Radiation From the Edge (MARFE) is a critical plasma instability that often precedes density-limit disruptions in tokamaks, posing a significant risk to…
Plasma Shape Control via Zero-shot Generative Reinforcement Learning
Niannian Wu, Rongpeng Li, Zongyu Yang +6
Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the nee…
FusionMAE: large-scale pretrained model to optimize and simplify diagnostic and control of fusion plasma
Zongyu Yang, Zhenghao Yang, Wenjing Tian +14
In magnetically confined fusion device, the complex, multiscale, and nonlinear dynamics of plasmas necessitate the integration of extensive diagnostic systems to effectively monito…
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…
Identifying L-H transition in HL-2A through deep learning
Meihuizi He, Songfen Liu, Fan Xia +2
During the operation of tokamak devices, addressing the thermal load issues caused by Edge Localized Modes (ELMs) eruption is crucial. Ideally, mitigation and suppression measures…