Enhancing disruption prediction through Bayesian neural network in KSTAR
Jinsu Kim, Jeongwon Lee, Jaemin Seo, Young-chul Ghim, Yeongsun Lee, Yong-Su Na
Abstract
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.