Artificial Intelligence/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)

Technical Report · Wed Sep 17 00:00:00 EDT 2025

DOI:https://doi.org/10.2172/2588687· OSTI ID:2588687

Lin, Zhihong Lin ^[1]

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1. Univ. of California, Irvine, CA (United States)

This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.

• Research Organization: Univ. of California, Irvine, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Fusion Energy Sciences (FES)
• DOE Contract Number: SC0023640
• OSTI ID: 2588687
• Report Number(s): SC0023640
• Country of Publication: United States
• Language: English

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
Deep Learning
FRNN
GTC
magnetic fusion
SGTC