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级托卡马克装置至关重要，因为偏滤器靶板上的热和粒子通量存在约束限制。数值模拟是理解和控制这些等离子体的重要工具，然而，对于实时应用或全参数扫描，目前只有简单近似方法能够满足计算速度要求。针对快速模拟器的缺失，我们采用神经偏微分方程代理模型——一种基于数据驱动的神经网络代理模型，其训练数据由经典数值方法生成的解构成。该代理模型近似一个时间步进算子，能够随时间的推进演化出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一维动态模型）作为生成数据的参考模型。DIV1D的计算域覆盖从X点（上游）到靶板的单维热通量管道。我们模拟了由上游密度斜坡驱动动力学过程的真实TCV偏滤器等离子体，并对快速瞬变过程进行了探索性展望。在统一框架下评估了最先进的神经PDE代理模型，并针对DIV1D数据特性进行了扩展。我们评估了：(1) 速度-精度权衡；(2) 非线性行为的复现；(3) 数据效率；(4) 参数的插值与外推。训练完成后，神经PDE代理模型能够以亚实时计算速度忠实近似DIV1D的偏滤器等离子体动力学：在所提配置下，计算2ms等离子体动力学过程仅需约0.63ms墙钟时间，比DIV1D快数个数量级。