eXplainable artificial intelligence applied to algorithms for disruption prediction in tokamak devices

eXplainable artificial intelligence applied to algorithms for disruption prediction in tokamak devices

Introduction: This work explores the use of eXplainable artificial intelligence (XAI) to analyze a convolutional neural network (CNN) trained for disruption prediction in tokamak devices and fed with inputs composed of different physical quantities.Methods: This work focuses on a reduced dataset con...

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Bibliographic Details
Main Authors: L. Bonalumi, E. Aymerich, E. Alessi, B. Cannas, A. Fanni, E. Lazzaro, S. Nowak, F. Pisano, G. Sias, C. Sozzi
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-05-01
Series:Frontiers in Physics
Subjects:
nuclear fusion
disruptions
tokamak
JET
CNN
XAI
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2024.1359656/full

Internet

https://www.frontiersin.org/articles/10.3389/fphy.2024.1359656/full

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