Andreas Gillgren

Doctoral Student at Astronomy and Plasmaphysics

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.

Source: chalmers.se
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Showing 9 publications

2024

Data-driven models in fusion exhaust: AI methods and perspectives

S. Wiesen, S. Dasbach, A. Kit et al
Nuclear Fusion. Vol. 64 (8)
Journal article
2024

High temporal resolution of pedestal dynamics via machine learning on density diagnostics

Diogo R. Ferreira, Andreas Gillgren, Andrei Osipov et al
Plasma Physics and Controlled Fusion. Vol. 66 (2)
Journal article
2023

A fast neural network surrogate model for the eigenvalues of QuaLiKiz

Emil Fransson, Andreas Gillgren, A. Ho et al
Physics of Plasmas. Vol. 30 (12)
Journal article
2022

Disruption prediction with artificial intelligence techniques in tokamak plasmas

J. Vega, A. Murari, S. Dormido-Canto et al
Nature Physics. Vol. 18 (7), p. 741-750
Review article
2022

Enabling adaptive pedestals in predictive transport simulations using neural networks

Andreas Gillgren, Emil Fransson, Dimitriy Yadykin et al
Nuclear Fusion. Vol. 62 (9)
Journal article
2022

Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

J. Garcia, Ye. O. Kazakov, J. Ongena et al
Nature Physics. Vol. 18 (7), p. 776-782
Journal article
2022

Overview of JET results for optimising ITER operation

J. Mailloux, N. Abid, K. Abraham et al
Nuclear Fusion. Vol. 62 (4)
Review article
2021

Towards understanding reactor relevant tokamak pedestals

C. J. Ham, A. Bokshi, D. Brunetti et al
Nuclear Fusion. Vol. 61 (9)
Journal article

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