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

Journal Article · · Fusion Science and Technology
 [1];  [1]
  1. Massachusetts Institute of Technology Plasma Science and Fusion Center, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

Not provided.

Research Organization:
General Atomics, San Diego, CA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
FC02-04ER54698; SC0014264
OSTI ID:
1540362
Journal Information:
Fusion Science and Technology, Vol. 74, Issue 1-2; ISSN 1536-1055
Publisher:
American Nuclear Society
Country of Publication:
United States
Language:
English

References (19)

Tokamak disruption alarm based on a neural network model of the high- beta limit June 1997
Disruption forecasting at JET using neural networks December 2003
Automatic disruption classification at JET: comparison of different pattern recognition techniques May 2006
Support vector machines for disruption prediction and novelty detection at JET October 2007
A prediction tool for real-time application in the disruption protection system at JET October 2007
Results of the JET real-time disruption predictor in the ITER-like wall campaigns October 2013
Statistical analysis of disruptions in JET April 2009
Detection of disruptions in the high- β spherical torus NSTX May 2013
Reconstruction of current profile parameters and plasma shapes in tokamaks November 1985
Real time equilibrium reconstruction for tokamak discharge control July 1998
MDS plus data acquisition system January 1997
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance May 2012
Integrated modeling applications for tokamak experiments with OMFIT July 2015
Random Forests January 2001
Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees February 2008
Unbiased and non-supervised learning methods for disruption prediction at JET April 2009
The Elements of Statistical Learning January 2009
Selecting and interpreting measures of thematic classification accuracy October 1997
Overview of manifold learning techniques for the investigation of disruptions on JET October 2014

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