用神经微分方程反推托卡马克等离子体控制所需的外部能量注入方案。
Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations
- 基于神经微分方程构建多节点等离子体动力学模型，实现逆向建模。
- 通过自动微分优化源输入，使模拟轨迹逼近目标温度与密度演化。
- 适用于当前及未来聚变装置的外部加热源设计，提升控制精度。
实现托卡马克中受控燃烧等离子体需精确调控外部粒子与能量源，以达到并维持靶心区密度与温度。本文提出一种基于神经常微分方程（Neural ODEs）的逆向建模方法，采用多节点等离子体动力学模型，给定如氘核密度或电子温度的目标时间演化轨迹，计算驱动等离子体趋近该行为的外部源分布（如中性束注入功率）。该方法在NeuralPlasmaODE框架中实现，可建模多区域、多时间尺度输运，并包含辐射、辅助加热和节点间能量交换等物理机制。将控制任务定义为优化问题，利用神经微分方程求解器的自动微分最小化模拟轨迹与目标轨迹间的偏差。该框架将正向仿真工具转化为面向控制的模型，为当前及未来聚变装置提供外部源配置的实用计算方法。
原文摘要 · Abstract (English)
Achieving controlled burning plasma in tokamaks requires precise regulation of external particle and energy sources to reach and maintain target core densities and temperatures. This work presents an inverse modeling approach using a multinodal plasma dynamics model based on neural ordinary differential equations (Neural ODEs). Given a desired time evolution of nodal quantities such as deuteron density or electron temperature, we compute the external source profiles, such as neutral beam injection (NBI) power, that drive the plasma toward the specified behavior. The approach is implemented within the NeuralPlasmaODE framework, which models multi-region, multi-timescale transport and incorporates physical mechanisms including radiation, auxiliary heating, and internodal energy exchange. By formulating the control task as an optimization problem, we use automatic differentiation through the Neural ODE solver to minimize the discrepancy between simulated and target trajectories. This framework transforms the forward simulation tool into a control-oriented model and provides a practical method for computing external source profiles in both current and future fusion devices.
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