Enhancing disruption prediction through Bayesian neural network in KSTAR
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...
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| Published in | Plasma physics and controlled fusion Vol. 66; no. 7; pp. 75001 - 75021 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IOP Publishing
01.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0741-3335 1361-6587 |
| DOI | 10.1088/1361-6587/ad48b7 |
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| 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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| AbstractList | 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. |
| Author | Lee, Jeongwon Kim, Jinsu Ghim, Young-Chul Lee, Yeongsun Na, Yong-Su Seo, Jaemin |
| Author_xml | – sequence: 1 givenname: Jinsu orcidid: 0009-0000-2610-4551 surname: Kim fullname: Kim, Jinsu organization: Department of Nuclear Engineering, Seoul National University , Seoul, Republic of Korea – sequence: 2 givenname: Jeongwon orcidid: 0000-0002-2353-2603 surname: Lee fullname: Lee, Jeongwon organization: Korean Institute of Fusion Energy , Daejeon, Republic of Korea – sequence: 3 givenname: Jaemin orcidid: 0000-0003-0635-0282 surname: Seo fullname: Seo, Jaemin organization: Chung-Ang University Department of Physics, Seoul, Republic of Korea – sequence: 4 givenname: Young-Chul orcidid: 0000-0003-4123-9416 surname: Ghim fullname: Ghim, Young-Chul organization: Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology , Daejeon, Republic of Korea – sequence: 5 givenname: Yeongsun orcidid: 0000-0003-4474-416X surname: Lee fullname: Lee, Yeongsun organization: Department of Nuclear Engineering, Seoul National University , Seoul, Republic of Korea – sequence: 6 givenname: Yong-Su orcidid: 0000-0001-7270-3846 surname: Na fullname: Na, Yong-Su organization: Department of Nuclear Engineering, Seoul National University , Seoul, Republic of Korea |
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| Title | Enhancing disruption prediction through Bayesian neural network in KSTAR |
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