Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D-Reference-Cited by-同舟云学术

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

Published:2018-03-07 Issue:1-2 Volume:74 Page:89-100
ISSN:1536-1055
Container-title:Fusion Science and Technology
language:en
Short-container-title:Fusion Science and Technology

Author:

Rea Cristina¹^ORCID,Granetz Robert S.¹^ORCID

Affiliation:

1. Massachusetts Institute of Technology Plasma Science and Fusion Center, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

Funder

U.S. Department of Energy

Publisher

Informa UK Limited

Subject

Mechanical Engineering,General Materials Science,Nuclear Energy and Engineering,Nuclear and High Energy Physics,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15361055.2017.1407206

Reference23 articles.

1. Tokamak disruption alarm based on a neural network model of the high- beta limit

2. Disruption forecasting at JET using neural networks

3. Automatic disruption classification at JET: comparison of different pattern recognition techniques

4. Support vector machines for disruption prediction and novelty detection at JET

5. A prediction tool for real-time application in the disruption protection system at JET

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1. High-beta disruption prediction study on HL-2A with instance-based transfer learning;Nuclear Fusion;2024-07-26

2. Cross-tokamak deployment study of plasma disruption predictors based on convolutional autoencoder;Plasma Physics and Controlled Fusion;2024-07-08

3. A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors;Nature Communications;2024-03-18

4. Data-driven disruption prediction using random forest in KSTAR;Fusion Engineering and Design;2024-02

5. A Review of Traditional and Data-Driven Approaches for Disruption Prediction in Different Tokamaks;E3S Web of Conferences;2024

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