Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
Fast surrogates for tokamak boundary-plasma simulation are typically deterministic regressors mapping a global operating point to a flattened vector of cell values. Near the divertor detachment transition the steady state is not reliably single-valued. A point estimate must average over qualitatively different plasma states, and it arrives with no statement of confidence. Moreover, the flattened vector representation discards the geometric structure of the SOLPS-ITER mesh. This work addresses both problems. We unroll the curvilinear mesh into three fixed-size image tensors whose layout preserves cell adjacency and inverts exactly, letting a convolutional network act on the geometry withou...
| Authors | Gabriele Gianuzzo, Stefan Dasbach, Fleur Hendriks et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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