Robust confinement state classification with uncertainty quantification through ensembled data-driven methods

Robust confinement state classification with uncertainty quantification through ensembled data-driven methods

Maximizing fusion performance in tokamaks relies on high energy confinement, often achieved through distinct operating regimes. The automated labeling of these confinement states is crucial to enable large-scale analyses or for real-time control applications. While this task becomes difficult to aut...

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Bibliographic Details
Main Authors: Yoeri Poels, Cristina Venturini, Alessandro Pau, Olivier Sauter, Vlado Menkovski, the TCV Team, the WPTE Team
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Nuclear Fusion
Subjects:
confinement state
H-mode
L-mode
classification
machine learning
uncertainty quantification
Online Access:https://doi.org/10.1088/1741-4326/adf349
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https://doi.org/10.1088/1741-4326/adf349

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