13 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
Agnes Korcsak-Gorzo, Charl Linssen, Jasper Albers +9
This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for em…
Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
Stefan Dasbach, Tom Tetzlaff, Markus Diesmann +1
The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and N…