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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.
arXiv:2607. 21407v1 Announce Type: cross Abstract: The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment.
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
arXiv:2602. 15084v2 Announce Type: replace-cross Abstract: We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous diagnostics from the publicly available MAST dataset.
arXiv:2608.23217v1 Announce Type: cross Abstract: Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solver...
Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We...