Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV

The rampdown in tokamak operations is a difficult to simulate phase during which the plasma is often pushed towards multiple instability limits. To address this challenge, and reduce the risk of disrupting operations, we leverage recent advances in Scientific Machine Learning (SciML) to develop a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. By integrating simple physics structure and data-driven models, the NSSM efficiently learns plasma dynamics during the rampdown from a modest dataset of 311 pulses with only five pulses in the reactor relevant high performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid multiple instability limits with high probability. Experiments at TCV ramping down high performance plasmas show statistically significant improvements in current and energy at plasma termination, with improvements in speed through continuous re-training. A predict-first experiment, increasing plasma current by 20\% from baseline, demonstrates the NSSM's ability to make small extrapolations with sufficient accuracy to design trajectories that successfully terminate the pulse. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty, and demonstrates the relevance of the SciML approach to learning plasma dynamics for rapidly developing robust trajectories and controls during the incremental campaigns of upcoming burning plasma tokamaks.托卡马克（tokamak）运行的斜坡降阶段是难以模拟的关键工况，在此阶段等离子体常被推向多重不稳定性极限。为应对该挑战并降低运行中断风险，研究团队借助近期科学机器学习（Scientific Machine Learning, SciML）领域的研究进展，开发了一款神经状态空间模型（neural state-space model, NSSM），用于预测可变构型托卡马克（Tokamak à Configuration Variable, TCV）斜坡降阶段的等离子体动力学特性。该模型通过整合简易物理结构与数据驱动模型，可从规模适中的311次脉冲数据集（其中仅5次属于反应堆相关的高性能运行区间）中高效学习斜坡降阶段的等离子体动力学行为。NSSM针对不确定性实现了并行化计算，并结合强化学习（reinforcement learning, RL）设计出可高概率规避多重不稳定性极限的运行轨迹。在TCV装置上开展的高性能等离子体斜坡降实验结果表明，等离子体终止时刻的电流与能量参数均获得了统计学意义上的显著提升，且通过持续再训练可进一步提升运算速度。一项以预测为先导的验证实验：将等离子体电流相较于基线水平提升20%，该实验证实NSSM具备足够精度完成小幅外推的能力，可用于设计可成功终止脉冲的运行轨迹。本研究提出的方法为设计具备较强不确定性鲁棒性的托卡马克控制系统铺平了道路，同时验证了SciML方法在等离子体动力学学习中的应用价值，可用于在未来燃烧等离子体托卡马克的渐进式试验阶段快速开发鲁棒运行轨迹与控制系统。