Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks

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.

Keywords:

• Disruption
• machine learning
• DIII-D, JET

Acknowledgments

The authors would like to thank T. Odstrcil for the tomographic reconstructions of the bolometer emissivity of DIII-D discharge 175697. This work was supported by the U.S. Department of Energy under DE-FC02-04ER54698 and DE-SC0014264. Part of the data analysis reported in this paper was performed using the OMFIT integrated modelling framework.³² The DIII-D data shown in this paper can be obtained in digital format by following the links at https://fusion.gat.com/global/D3D_DMP. This work has also been carried out within the framework of the EUROfusion Consortium and received funding from the EURATOM research and training programme 2014 2018 and 2019 2020 under grant agreement no. 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. Additionally, the Swiss Plasma Center authors are supported in part by the Swiss National Foundation.

This report was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.