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dc.contributor.authorChurchill, R.M-
dc.contributor.authorthe DIII-D team-
dc.description.abstractThe multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.en_US
dc.description.tableofcontentsreadme and digital data filesen_US
dc.publisherPrinceton Plasma Physics Laboratory, Princeton Universityen_US
dc.subjectplasma physicsen_US
dc.subjectmachine learningen_US
dc.subjectdeep learningen_US
dc.subjectconvolutional neural networksen_US
dc.titleDeep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devicesen_US
dc.contributor.funderU. S. Department of Energyen_US
Appears in Collections:Theory and Computation

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