While fusion reactors known as tokamaks hold promise as a firm energy source,
advances in plasma control, and handling of events where control of plasmas is
lost, are needed for them to be economical. A significant bottleneck towards
applying more advanced control algorithms is the need for better plasma
simulation, where both physics-based and data-driven approaches currently fall
short. The former is bottle-necked by both computational cost and the
difficulty of modelling plasmas, and the latter is bottle-necked by the
relative paucity of data. To address this issue, this work applies the neural
ordinary differential equations (ODE) framework to the problem of predicting a
subset of plasma dynamics, namely the coupled plasma current and internal
inductance dynamics. As the neural ODE framework allows for the natural
inclusion of physics-based inductive biases, we train both physics-based and
neural network models on data from the Alcator C-Mod fusion reactor and find
that a model that combines physics-based equations with a neural ODE performs
better than both existing physics-motivated ODEs and a pure neural ODE model.

通过将神经常微分方程 (ODE) 框架应用于等离子体动力学的问题，本研究从阿尔卡托 C-Mod 聚变反应堆的数据中训练出结合了基于物理方程和神经 ODE 的模型，发现其性能优于现有的基于物理的 ODE 和纯神经 ODE 模型。