Recent J-TEXT research has highlighted the significance of the role that non-axisymmetric
magnetic perturbations, so called three-dimensional (3D) magnetic perturbation (MP) fields …

A real-time disruption predictor using random forest was developed for high-density
disruptions and used in the plasma control system (PCS) of the EAST tokamak for the first …

In this study, a full convolutional neural network is trained on a large database of
experimental EAST data to classify disruptive discharges and distinguish them from non …

Increasing the plasma density is one of the key methods in achieving an efficient fusion
reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit …

Reliable disruption prediction (DP) and disruption mitigation systems are considered
unavoidable during international thermonuclear experimental reactor (ITER) operations and …

Tokamaks allow to confine fusion plasma with magnetic fields. The prediction/reconstruction
of the last closed-flux surface (LCFS) is one of the primary challenges in the control of the …

Using machine learning (ML) techniques to develop disruption predictors is an effective way
to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption …

Disruption prediction is essential for the safe operation of a large scale tokamak. Existing
disruption predictors based on machine learning techniques have good prediction …

One of the most pressing challenges facing the fusion community is adequately mitigating
or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done …

Abstract Machine learning research and applications in fusion plasma experiments are one
of the main subjects on J-TEXT. Since 2013, various kinds of traditional machine learning …