Continuous update of machine learning disruption prediction and prevention models at JET

The complex interplay of physics phenomena, which can cause plasma disruptions, hinders the development of predicting models. Recently, satisfactory Machine Learning predictors have been deployed on different devices. These models extract information from the high-dimensional data spaces of fusion experiments and help to detect and classify disruptions. Nevertheless, Machine Learning predictors have two main limitations: their performances deteriorate if the operating scenario evolves and they are difficult to interpret so that it is problematic to use them to study the physics of disruptions. This second reason motivated the development of interpretable Machine Learning algorithms, whose outputs can be interpreted in terms of the underlying physics. The GTM model, which is implemented on the PETRA system at JET, is an unsupervised mapping method, whose clusters can be colored using the knowledge over a set of suitably chosen plasma parameters. During the model training, this knowledge was given by manually identifying the beginning of the pre-disruptive phase of a selected set of disrupted discharges, which describes the disrupted operational space. Moreover, the disruption free operational space was described considering the flat-top phase of the plasma current for a selected set of regularly terminated discharges. The obtained GTM achieved very good performances and it was possible to study the evolution of its outputs by looking at the projection of the discharge over the map.