# Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

arXiv: https://arxiv.org/abs/1911.00149 · HTML: https://mlsanity.com/paper/1911.00149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network](https://mlsanity.com/paper/2410.08889/) (2024)

## Abstract (arXiv)

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 (~30k), achieving an F1-score of ~91% on individual time-slices using only the ECEi data.