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 …

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100
million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape …

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

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 …

The European nuclear fusion research community has elaborated a Roadmap to the
realisation of fusion energy in which 'ITER is the key facility and its success is the most …

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 …

An initial concept for the plasma diagnostic and control (D&C) system has been developed
as part of European studies towards the development of a demonstration tokamak fusion …

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak
devices is the necessity of avoiding disruptions of the plasma current due to instabilities. 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 …

In this paper, we discuss recent advances in deep convolutional neural networks (CNNs) for
sequence learning, which allow identifying long-range, multi-scale phenomena in long …