Visiting Graduate Student from Eindhoven University of Technology working on disruption predictors using machine learning

Education

MSc Science and Technology of Nuclear Fusion, Eindhoven University of Technology, The Netherlands (expected September 2018)

MSc Systems and Control, Eindhoven University of Technology , The Netherlands (expected September 2018)

BSc Mechanical Engineering, University of Twente, The Netherlands (2015)

Kornee used machine learning algorithms to create multiple disruption predictors. As a visiting master student he worked in the Plasma Control group of prof. Egemen. Kornee follows a double degree master program on both “Nuclear Fusion” and “Systems and Control” at Eindhoven University of Technology. The disruption predictors he created where initially aimed on the DIII-D fusion experiment, for which satisfying results were found (250 ms ahead prediction). Later also a start is made on creation of disruption predictors on NSTX data trying to achieve the same results.
Kornee presented these results and more at the  ‘Theory and Simulation of Disruptions Workshop’. Currently a paper written by Kornee on the DIII-D disruption predictor is under review.