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arXiv:2507.02897 (cs)
[Submitted on 21 Jun 2025]

Title:Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks

Authors:Nathaniel Chen, Cheolsik Byun, Azarakash Jalalvand, Sangkyeun Kim, Andrew Rothstein, Filippo Scotti, Steve Allen, David Eldon, Keith Erickson, Egemen Kolemen
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Abstract:While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D2 gas, we demonstrate feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image based diagnostic for regulatory compliant controller necessary for future fusion reactors.
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY); Plasma Physics (physics.plasm-ph)
Cite as: arXiv:2507.02897 [cs.LG]
  (or arXiv:2507.02897v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2507.02897
Related DOI: https://doi.org/10.1088/1741-4326/ae3972

Submission history

From: Nathaniel Chen [view email]
[v1] Sat, 21 Jun 2025 22:21:26 UTC (13,604 KB)
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