Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D

Integrated modeling applications for tokamak experiments with OMFIT

(2015) O. Meneghini et al.   NUCLEAR FUSION

Overview of manifold learning techniques for the investigation of disruptions on JET

(2014) B Cannas et al.   PLASMA PHYSICS AND CONTROLLED FUSION

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

(2013) Jesús Vega et al.   FUSION ENGINEERING AND DESIGN

Detection of disruptions in the high-βspherical torus NSTX

(2013) S.P. Gerhardt et al.   NUCLEAR FUSION

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

(2012) Carsten F. Dormann et al.   ECOGRAPHY

Statistical analysis of disruptions in JET

(2009) P.C. de Vries et al.   NUCLEAR FUSION

Unbiased and non-supervised learning methods for disruption prediction at JET

(2009) A. Murari et al.   NUCLEAR FUSION

Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees

(2008) A. Murari et al.   NUCLEAR FUSION