A real-time machine learning-based disruption predictor in DIII-D | Zendy

Cristina Rea | Zendy; Kevin Montes | Zendy; Keith Erickson | Zendy; R. Granetz | Zendy; R. A. Tinguely | Zendy

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A real-time machine learning-based disruption predictor in DIII-D

Author(s) -

Cristina Rea,

Kevin Montes,

Keith Erickson,

R. Granetz,

R. A. Tinguely

Publication year - 2019

Publication title -

nuclear fusion

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 1.774

H-Index - 120

eISSN - 1741-4326

pISSN - 0029-5515

DOI - 10.1088/1741-4326/ab28bf

Subject(s) - computer science , identification (biology) , alarm , diii d , feature (linguistics) , constant false alarm rate , millisecond , metric (unit) , false alarm , range (aeronautics) , warning system , signal (programming language) , real time computing , random forest , simulation , artificial intelligence , plasma , physics , tokamak , telecommunications , engineering , electrical engineering , linguistics , operations management , quantum mechanics , astronomy , aerospace engineering , biology , programming language , philosophy , botany

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