Enhancing Disruption Prediction through Bayesian Neural Network in KSTAR
arxiv(2023)
Abstract
Disruption in tokamak plasmas, stemming from various instabilities, poses a
critical challenge, resulting in detrimental effects on the associated devices.
Consequently, the proactive prediction of disruptions to maintain stability
emerges as a paramount concern for future fusion reactors. While data-driven
methodologies have exhibited notable success in disruption prediction,
conventional neural networks within a frequentist approach cannot adequately
quantify the uncertainty associated with their predictions, leading to
overconfidence. To address this limit, we utilize Bayesian deep probabilistic
learning to encompass uncertainty and mitigate false alarms, thereby enhancing
the precision of disruption prediction. 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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