17 citations · 37 across the 5 of their papers we have counts for
5 papers
Accelerated Bayesian inference of plasma profiles with self-consistent MHD equilibria at W7-X via neural networks
Andrea Merlo, Andrea Pavone, Daniel Böckenhoff +10
High- operations require a fast and robust inference of plasma parameters with a self-consistent MHD equilibrium. Precalculated MHD equilibria are usually employe…
Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios
Andrea Merlo, Daniel Böckenhoff, Jonathan Schilling +7
In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used t…
Bayesian modelling of multiple plasma diagnostics at Wendelstein 7-X
Sehyun Kwak, J. Svensson, S. Bozhenkov +7
Consistent inference of the electron density and temperature has been carried out with multiple heterogeneous plasma diagnostic data sets at Wendelstein 7-X. The predictive models…
Deep learning for Gaussian process tomography model selection using the ASDEX Upgrade SXR system
Francisco Matos, Jakob Svensson, Andrea Pavone +2
Gaussian process tomography (GPT) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak, given some model for the under…
Heat and particle flux detachment with stable plasma conditions in the Wendelstein 7-X stellarator fusion experiment
Marcin Jakubowski, Ralf König, Oliver Schmitz +30
Reduction of particle and heat fluxes to plasma facing components is critical to achieve stable conditions for both the plasma and the plasma material interface in magnetic confine…