Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Nuclear fusion power delivered by magnetic-confinement tokamak reactors holds the
promise of sustainable and clean energy. The avoidance of large-scale plasma instabilities …

This article reviews applications of Bayesian inference and machine learning (ML) in
nuclear fusion research. Current and next-generation nuclear fusion experiments require …

Progress in the area of MHD stability and disruptions, since the publication of the 1999 ITER
Physics Basis document (1999 Nucl. Fusion 39 2137–2664), is reviewed. Recent theoretical …

Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is
a violent event that terminates a confined plasma and causes unacceptable damage to the …

A survey has been carried out into the causes of all 2309 disruptions over the last decade of
JET operations. The aim of this survey was to obtain a complete picture of all possible …

A disruption prediction algorithm, called disruption prediction using random forests (DPRF),
has run in real-time in the DIII-D plasma control system (PCS) for more than 900 discharges …

This paper reports on disruption prediction using a shallow machine learning method known
as a random forest, trained on large databases containing only plasma parameters that are …

In view of the future high power nuclear fusion experiments, the early identification of
disruptions is a mandatory requirement, and presently the main goal is moving from the …

Using data-driven methodology, we exploit the time series of relevant plasma parameters for
a large set of disrupted and non-disrupted discharges to develop a classification algorithm …