In a sequential regression setting, a decision-maker may be primarily concerned with
whether the future observation will increase or decrease compared to the current one, rather …

Active feedback control in magnetic confinement fusion devices is desirable to mitigate
plasma instabilities and enable robust operation. Optical high-speed cameras provide a …

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 drive to control tokamaks, a prominent technology in nuclear fusion, is essential due to
its potential to provide a virtually unlimited source of clean energy. Reinforcement learning …

Abstract Machine learning classifiers typically provide scores for the different classes. These
scores are supplementary to class predictions and may be crucial for downstream decision …

Methodologies for combining the accuracy of data-driven models with extrapolability of
physics-based models are described and tested, for the task of building transport models of …

In this paper we propose a modular approach, based on a deep reinforcement learning
technique, for the control of a plasma with a limited configuration in the DEMO tokamak …

A unified AI (artificial intelligence) approach to predict and control the dynamics of kinetic
plasma profiles in fusion reactors is presented. On one hand, it is demonstrated that …

Due to the complexity, chaotic behavior, and non-linear nature of tokamak plasma dynamics,
modeling tokamak discharges poses a formidable challenge. This modeling task …

In fusion research, the tokamak is vital for stabilizing plasma and fostering nuclear fusion
through magnetic confinement. The digital tokamak, which has emerged with increased …