Plasma Confinement Mode Classification Using a Sequence-to-Sequence Neural Network With Attention

TL;DR

This paper introduces an attention-based sequence-to-sequence neural network for classifying plasma confinement modes in tokamak experiments, outperforming previous Conv-RNN models and improving automatic detection accuracy.

Contribution

The paper presents a novel sequence-to-sequence model with attention for plasma mode classification, addressing noise sensitivity and decision limitations of prior Conv-RNN approaches.

Findings

Sequence-to-sequence model outperforms Conv-RNN in classification accuracy.

Attention mechanism improves model robustness to label noise.

Model achieves high scores on both training and testing datasets.

Abstract

In a typical fusion experiment, the plasma can have several possible confinement modes. At the TCV tokamak, aside from the Low (L) and High (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing methods that automatically detect these modes is considered to be important for future tokamak operation. Previous work with deep learning methods, particularly convolutional recurrent neural networks (Conv-RNNs), indicates that they are a suitable approach. Nevertheless, those models are sensitive to noise in the temporal alignment of labels, and that model in particular is limited to making individual decisions taking into account only its own hidden state and its input at each time step. In this work, we propose an architecture for a sequence-to-sequence neural network model with attention which solves both of those issues. Using a carefully calibrated dataset, we compare the performance of a Conv-RNN with that of our proposed sequence-to-sequence model, and show two results: one, that the Conv-RNN can be improved upon with new data; two, that the sequence-to-sequence model can improve the results even further, achieving excellent scores on both train and test data.