Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices
- Princeton University (PPPL)
The 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.
- Research Organization:
- Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-09CH11466; FC02-04ER54698; FG02-99ER54531
- OSTI ID:
- 1661171
- Country of Publication:
- United States
- Language:
- English