Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural PDE surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D's domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D's divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2ms of plasma dynamics can be computed in $\approx$0.63ms of wall-clock time, several orders of magnitude faster than DIV1D.

翻译：管理偏滤器等离子体对于运行反应堆规模托卡马克装置至关重要，这源于偏滤器靶板上的热流和粒子通量限制。模拟是理解和控制这些等离子体的重要工具，然而，对于实时应用或参数全面扫描，目前只有简单的近似方法能够达到足够快的速度。我们通过神经PDE替代模型（基于数据驱动的神经网络替代模型，利用经典数值方法生成的解进行训练）来解决缺乏快速模拟器的问题。该替代模型近似于一个时间步进算子，该算子能够随时间演化参考物理模型的全空间解。我们使用DIV1D（一种偏滤器等离子体的一维动态模型）作为参考模型来生成数据。DIV1D的计算域覆盖从X点（上游）到靶板的单维热通量管。我们模拟了一个由上游密度斜坡驱动的实际TCV偏滤器等离子体动力学过程，并提供了针对快速瞬态现象的探索性展望。最先进的神经PDE替代模型在统一框架下进行评估，并针对DIV1D数据的特性进行了扩展。我们评估了：（1）速度与精度的权衡；（2）非线性行为的复现能力；（3）数据效率；以及（4）参数的插值与外推能力。一旦训练完成，神经PDE替代模型能够在亚实时计算速度下忠实近似DIV1D的偏滤器等离子体动力学：在建议配置中，计算2毫秒的等离子体动力学过程仅需约0.63毫秒的挂钟时间，比DIV1D快数个数量级。