用可解释的AI实现托卡马克偏滤器脱落的实时控制，误差仅2%。
Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks
- 基于图像的线性可解释模型，实现实时反馈控制。
- 脱附与再附着控制均保持2%的平均绝对误差。
- 适合需要合规验证的未来聚变堆控制系统开发。
尽管人工智能在核聚变控制中前景广阔，但其黑箱特性使其在监管环境中难以合规部署。本研究基于DIII-D下偏滤器相机，实现了并验证了一种实时、线性且可解释的AI控制框架，成功完成偏滤器脱附控制。通过注入氘气（D2），在脱附和再附着过程中均实现了与目标值相差仅2%的平均绝对误差。该自动训练与线性处理框架可扩展至任意基于图像的诊断系统，为未来聚变反应堆所需的合规控制器提供技术路径。
原文摘要 · Abstract (English)
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.
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