2020/05/11 by Joost Croonen, Croonen, Joost, Jorge Amaya +3
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #FOS: Physical sciences #Magnetic confinement fusion research #Network Security and Intrusion Detection #Plasma Physics (physics.plasm-ph) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.05139
openalex publication_date 2020/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Disruption prediction and mitigation is of key importance in the development of sustainable tokamakreactors. Machine learning has become a key tool in this endeavour. In this paper multiple machinelearning models will be tested and compared. A particular focus has been placed on their portability.This describes how easily the models can be used with data from new devices. The methods used inthis paper are support vector machine, 2-tiered support vector machine, random forest, gradient boostedtrees and long-short term memory. The results show that the support vector machine performanceis marginally better among the standard models, while the gradient boosted trees performed the worst.The portable variant of each model had lower performance. Random forest obtained the highest portableperformance. Results also suggest that disruptions can be detected as early as 600ms before the event.An analysis of the computational cost showed all models run in less than 1ms, allowing sufficient timefor disruption mitigation.