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On learning latent dynamics of the AUG plasma state

Author
Abstract

In this work, we demonstrate the utility of state representation learning applied to modeling the time evolution of electron density and temperature profiles at ASDEX-Upgrade (AUG). The proposed model is a deep neural network, which learns to map the high dimensional profile observations to a lower dimensional state. The mapped states, alongside the original profile's corresponding machine parameters, are used to learn a forward model to propagate the state in time. We show that this approach is able to predict AUG discharges using only a selected set of machine parameters. The state is then further conditioned to encode information about the confinement regime, which yields a simple baseline linear classifier, while still retaining the information needed to predict the evolution of profiles. We, then, discuss the potential use cases and limitations of state representation learning algorithms applied to fusion devices.

Year of Publication
2024
Journal
Physics of Plasmas
Volume
31
Issue
3
Number of Pages
032504
DOI
10.1063/5.0174128
PId
3b35d3c10f9152c7096fb2f121ece9a2
Alternate Journal
Phys. Plasmas
Label
OA
Journal Article
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