用机器学习预测托卡马克放电下降阶段等离子体行为，提升运行稳定性。
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
- 构建神经状态空间模型，结合物理规律与数据驱动预测等离子体动态。
- 仅用311次脉冲数据训练，实现20%电流提升下的稳定控制。
- 适合关注核聚变控制、科学机器学习应用的研究者。
托卡马克放电下降阶段难以模拟，常引发多种等离子体不稳定性。为降低运行中断风险，本文结合科学机器学习（SciML）方法，构建神经状态空间模型（NSSM），用于预测托卡马克 à Configuration Variable（TCV）装置在放电下降期间的等离子体演化。该模型仅基于311次脉冲数据训练，其中仅有5次处于反应堆相关高性能工况。通过并行处理不确定性，结合强化学习（RL）设计避开不稳定性边界的控制轨迹。在TCV上的高性能实验表明，相关指标显著改善。一次“先预测后实验”测试中，等离子体电流较基线提升20%，验证了模型的小规模外推能力。该方法为设计对不确定性具有鲁棒性的托卡马克控制策略提供路径，并展示了科学机器学习在聚变实验中的实际价值。
原文摘要 · Abstract (English)
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM's ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.
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