Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes. (1st October 2022)

• Record Type: Journal Article
• Authors: Kaptanoglu, Alan A.; Jalalvand, Azarakhsh; Garcia, Alvin V.; Austin, Max E.; Verdoolaege, Geert; Schneider, Jeff; Hansen, Christopher J.; Brunton, Steven L.; Heidbrink, William W.; Kolemen, Egemen
• Is Part Of: Nuclear fusion. Volume 62:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: machine learning -- Alfvén eigenmodes -- energetic particles -- tokamak; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/1741-4326/ac8a03
• Languages: English
• ISSNs: 0029-5515

Abstract

Abstract: Alfvén eigenmodes (AEs) are an important and complex class of plasma dynamics commonly observed in tokamaks and other plasma devices. In this work, we manually labeled a small database of 26 discharges from the DIII-D tokamak in order to train simple neural-network-based models for classifying AEs. The models provide spatiotemporally local identification of four types of AEs by using an array of 40 electron cyclotron emission (ECE) signals as inputs. Despite the minimal dataset, this strategy performs well at spatiotemporally localized classification of AEs, indicating future opportunities for more sophisticated models and incorporation into real-time control strategies. The trained model is then used to generate spatiotemporally-resolved labels for each of the 40 ECE measurements on a much larger database of 1112 DIII-D discharges. This large set of precision labels can be used in future studies for advanced deep predictors and new physical insights.