克星
人工神经网络
贝叶斯概率
计算机科学
人工智能
机器学习
物理
核物理学
等离子体
托卡马克
作者
Jinsu Kim,Jeongwon Lee,Jaemin Seo,Young-chul Ghim,Yeongsun Lee,Yong-Su Na
出处
期刊:Plasma Physics and Controlled Fusion
[IOP Publishing]
日期:2024-05-08
卷期号:66 (7): 075001-075001
被引量:1
标识
DOI:10.1088/1361-6587/ad48b7
摘要
Abstract In this research, we develop a data-driven disruption predictor based on Bayesian deep probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR. Unlike conventional neural networks within a frequentist approach, Bayesian neural networks can quantify the uncertainty associated with their predictions, thereby enhancing the precision of disruption prediction by mitigating false alarm rates through uncertainty thresholding. Leveraging 0D plasma parameters from EFIT and diagnostic data, a temporal convolutional network adept at handling multi-time scale data was utilized. The proposed framework demonstrates proficiency in predicting disruptions, substantiating its effectiveness through successful applications to KSTAR experimental data.
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