Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak

Nuclear Fusion 2026 Open access

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Authors: Nathaniel Chen, C.S. Byun, Azarakhsh Jalalvand, S.K. Kim, Andrew Rothstein, F. Scotti, S. L. Allen, D. Eldon, Keith Erickson, Egemen Kolemen
DOI: 10.1088/1741-4326/ae3972
Cited by: 2
References: 41

Abstract

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 D 2 gas, we demonstrate successful 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 future fusion reactors.

Fields of Study & Subjects

Magnetic confinement fusion research

Publication details

Journal / source
Nuclear Fusion
Publisher
IOP Publishing
Volume
66
Issue
3
Pages
036014–036014
Publication date
16 January 2026
Language
EN