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...
| Published in: | Frontiers in Physics |
|---|---|
| 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
|
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1359656/full |
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