Magnetic confinement nuclear fusion holds great promise as a source of clean and
sustainable energy for the future. However, achieving net energy from fusion reactors …

To produce fusion reactions efficiently, thermonuclear plasmas have to reach extremely high
temperatures, which is incompatible with their coming into contact with material surfaces …

Nowadays, disruption predictors, based on machine learning techniques, can perform well
but they typically do not provide any information about the type of disruption and cannot …

Recent experience with metallic devices operating in ITER relevant regions of the
operational space, has shown that the disruptivity of these plasmas is unacceptably high …

In metallic devices, various forms of radiation collapse are one of the major causes of
plasma degradation leading to disruptions. Some of the most advanced scenarios, with …

Featured Application Machine-learning-based techniques have been applied to disruption
prediction in Tokamaks and, by symbolic regression via genetic programming, physically …

In recent years, a new tomographic inversion method based on the Maximum Likelihood
(ML) approach has been adapted to JET bolometry. Apart from its accuracy and reliability …

Bolometric tomography is a widely applied technique to infer important indirect quantities in
magnetically confined plasmas, such as the total radiated power. However, being an inverse …

We have trained a fully convolutional spatio-temporal model for fast and accurate
representation learning in the challenging exemplar application area of fusion energy …

The prediction of a disruptive event is a fundamental task for future fusion reactors. On
current tokamaks, most remedial actions have the aim of mitigating their effects, but in future …