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Neural Network-Based Confinement Mode Prediction for Real-Time Disruption Avoidance

Journal Article · · IEEE Transactions on Plasma Science
 [1];  [2];  [2];  [2];  [3]
  1. Advanced Computing, General Atomics, San Diego, CA, USA; OSTI
  2. Dynamics and Control, General Atomics, San Diego, CA, USA
  3. Magnetic Fusion Energy (MFE) Operations, General Atomics, San Diego, CA, USA
Not provided.
Research Organization:
General Atomics, San Diego, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
FC02-04ER54698
OSTI ID:
2418872
Journal Information:
IEEE Transactions on Plasma Science, Journal Name: IEEE Transactions on Plasma Science Journal Issue: 11 Vol. 50; ISSN 0093-3813
Publisher:
IEEE
Country of Publication:
United States
Language:
English

References (20)

Power requirement for accessing the H-mode in ITER journal July 2008
Real time equilibrium reconstruction for tokamak discharge control journal July 1998
Classification of tokamak plasma confinement states with convolutional recurrent neural networks journal February 2020
Current State of DIII-D Plasma Control System journal January 2020
Predicting disruptive instabilities in controlled fusion plasmas through deep learning journal April 2019
Temporal Convolutional Networks for Action Segmentation and Detection conference July 2017
Survey of disruption causes at JET journal April 2011
Disruptions, disruptivity and safer operating windows in the high- β spherical torus NSTX journal April 2013
Prospects for Disruption Handling in a Tokamak-Based Fusion Reactor journal April 2021
Analyses of ITER operation mode using the support vector machine technique for plasma discharge classification journal April 2008
Chapter 3: MHD stability, operational limits and disruptions journal June 2007
A design retrospective of the DIII-D tokamak journal May 2002
Progress in disruption prevention for ITER journal June 2019
Focal Loss for Dense Object Detection journal February 2020
Avoidance of vertical displacement events in DIII-D using a neural network growth rate estimator journal August 2021
Chapter 2: Plasma confinement and transport journal June 2007
Development and experimental qualification of novel disruption prevention techniques on DIII-D journal October 2021
A real-time machine learning-based disruption predictor in DIII-D journal July 2019
An application of survival analysis to disruption prediction via Random Forests journal August 2019
A review of theories of the L-H transition journal December 1999

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