Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks. (16th November 2020)

• Record Type: Journal Article
• Authors: Rea, C.; Montes, K. J.; Pau, A.; Granetz, R. S.; Sauter, O.
• Is Part Of: Fusion science and technology. Volume 76:Number 8(2020)
• Page Start: 912
• Page End: 924
• Publication Date: 2020-11-16
• Subjects: Disruption -- machine learning -- DIII-D, JET; Fusion reactors -- Periodicals; Nuclear fusion -- Periodicals; Fusion reactors; Nuclear fusion; Periodicals; 621.48405
• DOI: 10.1080/15361055.2020.1798589
• Languages: English
• ISSNs: 1536-1055

Abstract

Abstract: In this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.