On learning latent dynamics of the AUG plasma state

TL;DR

This paper applies deep neural networks to learn low-dimensional representations of plasma profiles in AUG, enabling accurate prediction of their evolution and confinement regimes using machine parameters.

Contribution

It introduces a novel deep learning approach for modeling plasma state dynamics and confinement regimes in fusion devices, enhancing interpretability and predictive capabilities.

Findings

Successful prediction of plasma profiles evolution

Encoding of confinement regimes in learned states

Potential for improved plasma control strategies

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.