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In a typical fusion experiment, the plasma can have several possibleconfinement 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 beimportant for future tokamak operation. Previous work with deep learningmethods, particularly convolutional recurrent neural networks (Conv-RNNs),indicates that they are a suitable approach. Nevertheless, those models aresensitive to noise in the temporal alignment of labels, and that model inparticular is limited to making individual decisions taking into account onlyits own hidden state and its input at each time step. In this work, we proposean architecture for a sequence-to-sequence neural network model with attentionwhich solves both of those issues. Using a carefully calibrated dataset, wecompare the performance of a Conv-RNN with that of our proposedsequence-to-sequence model, and show two results: one, that the Conv-RNN can beimproved upon with new data; two, that the sequence-to-sequence model canimprove the results even further, achieving excellent scores on both train andtest data.

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