Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations

The paper presents a probabilistic neural surrogate for scrape‑off‑layer (SOL) plasma simulations in tokamaks. By mapping the curvilinear SOLPS‑ITER mesh to three fixed‑size image tensors, the authors preserve geometric adjacency and enable a convolutional network to process the mesh without loss of information. A conditional flow‑matching model is trained on this representation, producing efficient, scalable predictions that capture multiple plausible outcomes—such as distinct hot and cold modes—near the divertor detachment transition and correctly recover known bifurcations in synthetic data.

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