Andreas Gillgren
Andreas is a PhD Student in Plasma Physics and Fusion Energy. His research focuses on Machine Learning and its applications for magnetic confined fusion devices.
Showing 8 publications
High temporal resolution of pedestal dynamics via machine learning on density diagnostics
A fast neural network surrogate model for the eigenvalues of QuaLiKiz
Machine learning applications for predicting the pedestal in tokamak plasmas
Disruption prediction with artificial intelligence techniques in tokamak plasmas
Enabling adaptive pedestals in predictive transport simulations using neural networks
Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions
Overview of JET results for optimising ITER operation
Towards understanding reactor relevant tokamak pedestals
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