Automatic disruption classification based on manifold learning for real-time applications on JET

Disruptions remain the biggest threat to the safe operation of tokamaks. To efficiently mitigate the negative effects, it is now considered important not only to predict their occurrence but also to be able to determine, with high probability, the type of disruption about to occur. This paper reports the results obtained using the nonlinear generative topographic map manifold learning technique for the automatic classification of disruption types. It has been tested using an extensive database of JET discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The success rate of the classification is extremely high, sometimes reaching 100%, and therefore the prospects for the deployment of this tool in real time are very promising.