13 citations · 14 across the 5 of their papers we have counts for
3 papers · 1 filter
Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
Gabriele Gianuzzo, Stefan Dasbach, Fleur Hendriks +2
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 divert…
Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas
Abdourahmane Diaw, Sebastian De Pascuale, Jae-Sun Park +3
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…
Deep-Learning based surrogate models for plasma exhaust simulations -- SOLPS-NN
Stefan Dasbach, Sebastijan Brezinsek, Yunfeng Liang +2
Accurate models of the scrape-off layer are required for the design and operation of tokamak fusion reactors. Scrape-off layer simulations are computationally expensive, difficult…