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 …

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

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to
understand and describe their behavior. However, there is a scarcity of plasma models of …

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 …

In this study, a long short-term memory (LSTM) model is trained on a large disruption
warning database to predict the disruption on EAST tokomak. To compare the performance …

Recent progress in the application of machine learning (ML)/artificial intelligence (AI)
algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for …

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 …

Key plasma physics and real-time control elements needed for robustly stable operation of
high fusion power discharges in ITER have been demonstrated in recent research …