Author

S. Dasbach

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#machine learning Preprint Sep 2026

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 s...

Gabriele Gianuzzo, S. Dasbach, Fleur Hendriks et al. · 0 citations

Jul 2026

Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas

The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment. Predicting these quantities is essential for operating current and future devices, but edge simulations that resolve them are too slow for parameter scans, optimizatio...

A. Diaw, S. de Pascuale, Jae-Sun Park et al. · arXiv.org · 0 citations