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 …

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 …

Real-time feedback control based on machine learning algorithms (MLA) was successfully
developed and tested on DIII-D plasmas to avoid tearing modes and disruptions while …

A new, fully data-driven algorithm has been developed that uses a neural network to predict
plasma profiles on a scale of τ E into the future given an actuator trajectory and the plasma …

Detecting disruptions with sufficient anticipation time is essential to undertake any form of
remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning …

Using data-driven methodology, we exploit the time series of relevant plasma parameters for
a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the …

The use of deep learning is facilitating a wide range of data processing tasks in many areas.
The analysis of fusion data is no exception since there is a need to process large amounts of …

Notwithstanding the efforts exerted over many years, disruptions remain a major impediment
on the route to a magnetic confinement reactor of the tokamak type. Machine learning …

In this paper, a neural predictor has been built using plasma discharges selected from two
years of ASDEX Upgrade experiments, from July 2002 to July 2004. In order to test the real …