Interpretation of machine-learning-based disruption models for plasma control

While machine learning techniques have been applied within the context of fusion for predicting plasma disruptions in tokamaks, they are typically interpreted with a simple 'yes/no' prediction or perhaps a probability forecast. These techniques take input signals, which could be real-time signals from machine diagnostics, to make a prediction of whether a transient event will occur. A major criticism of these methods is that, due to the nature of machine learning, there is no clear correlation between the input signals and the output prediction result. Here is proposed a simple method that could be applied to any existing prediction model to determine how sensitive the state of a plasma is at any given time with respect to the input signals. This is accomplished by computing the gradient of the decision function, which effectively identifies the quickest path away from a disruption as a function of the input signals and therefore could be used in a plasma control setting to avoid them. A numerical example is provided for illustration based on a support vector machine model, and the application to real data is left as an open opportunity.