A machine learning approach to interpreting complex high-dimensional spaces in Fusion Research

The ever-increasing volume of multidimensional data generated in the framework of fusion experiments, combined with the exponential growth of computing power and cloud computing technologies, has motivated researchers to explore advanced Statistical and Machine Learning (ML) techniques to solve complex problems. One aspect of particular interest in fusion research is the interpretability of ML models, that is, the capability to explain the connections between the knowledge extracted from data and the output of what has always been considered as a black-box. This contribution summarizes the efforts in the development of an innovative machine learning approach for the investigation of complex high-dimensional spaces and its exploitation as pattern recognition model to predict and classify disruptions on Tokamaks. The tool implemented for the analysis is based on the Generative Topographic Mapping (GTM), a generative model belonging to the class of manifold learning techniques. The advanced visualization capabilities of the tool together with its potential real-time application allow exploiting GTM algorithm for plasma monitoring, identifying patterns which reflect physics mechanisms leading to disruptions.