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Managing divertor plasmas is crucial for operating reactor scale tokamakdevices 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 simpleapproximations are currently fast enough. We address this lack of fastsimulators using neural PDE surrogates, data-driven neural network-basedsurrogate models trained using solutions generated with a classical numericalmethod. The surrogate approximates a time-stepping operator that evolves thefull spatial solution of a reference physics-based model over time. We useDIV1D, a 1D dynamic model of the divertor plasma, as reference model togenerate 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 withdynamics induced by upstream density ramps and provide an exploratory outlooktowards fast transients. State-of-the-art neural PDE surrogates are evaluatedin a common framework and extended for properties of the DIV1D data. Weevaluate (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 plasmadynamics 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.

Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates