A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas

Abstract Experimental observations assisted by 2-D imaging diagnostics on the KSTAR tokamak show that a solitary perturbation (SP) emerges prior to a boundary burst of magnetized toroidal plasmas, which puts forward SP as a potential candidate for the burst trigger. We have constructed a machine lea...

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Autores principales:	J. E. Lee, P. H. Seo, J. G. Bak, G. S. Yun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/95ab80ccca8441a49a1bf962fef640bd
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spelling	oai:doaj.org-article:95ab80ccca8441a49a1bf962fef640bd2021-12-02T14:11:32ZA machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas10.1038/s41598-021-83192-22045-2322https://doaj.org/article/95ab80ccca8441a49a1bf962fef640bd2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83192-2https://doaj.org/toc/2045-2322Abstract Experimental observations assisted by 2-D imaging diagnostics on the KSTAR tokamak show that a solitary perturbation (SP) emerges prior to a boundary burst of magnetized toroidal plasmas, which puts forward SP as a potential candidate for the burst trigger. We have constructed a machine learning (ML) model based on a convolutional deep neural network architecture for a statistical study to identify the SP as a boundary burst trigger. The ML model takes sequential signals detected from 19 toroidal Mirnov coils as input and predicts whether each temporal frame corresponds to an SP. We trained the network in a supervised manner on a training set consisting of real signals with manually annotated SP locations and synthetic burst signals. The trained model achieves high performances in various metrics on a test data set. We also demonstrated the reliability of the model by visualizing the discriminative parts of the input signals that the model recognizes. Finally, we applied the trained model to new data from KSTAR experiments, which were never seen during training, and confirmed that the large burst at the plasma boundary that can fatally damage the fusion device always involves the emergence of SP. This result suggests that the SP is a key to understanding and controlling of the boundary burst in magnetized toroidal plasmas.J. E. LeeP. H. SeoJ. G. BakG. S. YunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q J. E. Lee P. H. Seo J. G. Bak G. S. Yun A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
description	Abstract Experimental observations assisted by 2-D imaging diagnostics on the KSTAR tokamak show that a solitary perturbation (SP) emerges prior to a boundary burst of magnetized toroidal plasmas, which puts forward SP as a potential candidate for the burst trigger. We have constructed a machine learning (ML) model based on a convolutional deep neural network architecture for a statistical study to identify the SP as a boundary burst trigger. The ML model takes sequential signals detected from 19 toroidal Mirnov coils as input and predicts whether each temporal frame corresponds to an SP. We trained the network in a supervised manner on a training set consisting of real signals with manually annotated SP locations and synthetic burst signals. The trained model achieves high performances in various metrics on a test data set. We also demonstrated the reliability of the model by visualizing the discriminative parts of the input signals that the model recognizes. Finally, we applied the trained model to new data from KSTAR experiments, which were never seen during training, and confirmed that the large burst at the plasma boundary that can fatally damage the fusion device always involves the emergence of SP. This result suggests that the SP is a key to understanding and controlling of the boundary burst in magnetized toroidal plasmas.
format	article
author	J. E. Lee P. H. Seo J. G. Bak G. S. Yun
author_facet	J. E. Lee P. H. Seo J. G. Bak G. S. Yun
author_sort	J. E. Lee
title	A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
title_short	A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
title_full	A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
title_fullStr	A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
title_full_unstemmed	A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
title_sort	machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/95ab80ccca8441a49a1bf962fef640bd
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A machine learning approach to identify the universality of solitary perturbations accompanying boundary bursts in magnetized toroidal plasmas

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