## 1 Disruptions in Tokamaks: An Operational Perspective
This limit is typically represented as a function of the parameter I-\(l_{j}\)/(\(a\)B\({}_{\text{T}}\)), where I is the plasma current, \(l_{i}\) the internal inductance and \(a\) the minor radius. The \(\beta\)-limit plot for various campaigns of JET with the ILW is reported in Figure 2. Again, inspection of the plot reveals that, in this space, it is practically impossible to separate the disruptive from the safe operational regions. Therefore, from a practical point of view, these representations have poor predictive and interpretative capability, at least for JET with the ILW. The inadequacies of theoretical and empirical models of disruptions have motivated the development of data driven predictors. From this perspective, several machine learning methods have been developed. They cover a wide range of techniques from Fuzzy logic classifiers to neural networks [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. In recent years, a new predictor, Advanced Predictor Of DISruptions (APODIS), based on Support Vector Machines (SVMs), has been deployed in the JET real-time network and has performed very satisfactorily in terms of both success rate and false alarms, in a long series of campaigns without any need for retraining [7]. Manifold learning tools, such as Self Organising Maps and Generative Topographic Maps have provided very good results also for the task of  
Figure 1: Hugill plot for a large database of JET with the ITER-like Wall (ILW) covering campaigns C29–C30. The disruptive and safe discharges overlap completely in this space. For the non-disruptive discharges, 14 samples during the flat top are shown. For the disruptive discharges, 14 samples, taken during the 210 ms before the beginning of the current quench, are reported. Figure 2: The beta limit plot for a large database of JET with the ILW covering campaigns C29–C30. In addition, in this space the disruptive and safe discharges overlap completely. For the non-disruptive discharges, 14 samples during the flat top are shown. For the disruptive discharges, 14 samples, taken during the 210 ms before the beginning of the current quench, are reported. automatically determining the disruption type, many tens of ms in advance of the beginning of the current quench [16; 17]. Some of these classifiers have implemented a new metric based on the Geodesic Distance on Gaussian manifolds [18]. Even if these data-driven tools are producing quite impressive results, their main problem, particularly from the perspective of next step devices, is the number of examples required for training. In large machines such as ITER, it would be practically impossible to collect hundreds of disruptions to train the most satisfactorily performing machine learning tools such as APODIS. In the last couple of years, therefore, many efforts have been devoted to the developments of parsimonious data-driven techniques that can provide a good success rate of prediction after a few tens of disruptions and even after the first disruption [19; 20; 21]. Their future application is very promising, particularly if coupled with new methods to avoid disruptions [22; 23; 24] and to model the stability boundary [25]. The main remaining issues, with these advanced Machine Learning (ML) tools, are now their physics fidelity and the interpretability of their results. They have shown the potential to learn very efficiently from few examples, but they operate in such a way that their models do not necessarily reflect the dynamics behind the phenomenon. Since the resulting models are also very difficult to interpret, in the present format machine learning tools cannot really contribute to the interpretation of the physics behind disruptions.