Bayesian neural networks for predicting tokamak energy confinement time with uncertainty quantification

Bayesian neural networks for predicting tokamak energy confinement time with uncertainty quantification

Accurate estimation of the tokamak energy confinement time ( τ _E ) is crucial for optimizing the operation and design of fusion devices. Traditional methods, such as the ITER scaling law, often lack predictive precision and provide limited uncertainty quantification. To address these limitations, w...

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Main Authors: Enliang Gao, Chenguang Wan, Youjun Hu, Minglong Wang, Jingjing Lu, Zhisong Qu, Xinghao Wen, Jia Huang, Ying Chen, Heru Guo, Zhengping Luo, Zhi Yu, Xiaojuan Liu, Qiping Yuan, Jiangang Li
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Nuclear Fusion
Subjects:
tokamak
energy confinement time
Bayesian neural networks
uncertainty quantification
Online Access:https://doi.org/10.1088/1741-4326/ade8fd
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https://doi.org/10.1088/1741-4326/ade8fd

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