AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS) (Final Technical Report)

Luhmann, Neville [Univ. of California, Davis, CA (United States)] (ORCID:0000000206132931); Zhu, Yilun [Univ. of California, Davis, CA (United States)] (ORCID:0000000328428474)

doi:10.2172/3005512

DOE OSTI · 3005512

AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS) (Final Technical Report)

Luhmann, Neville [Univ. of California, Davis, CA (United States)] (ORCID:0000000206132931)·Zhu, Yilun [Univ. of California, Davis, CA (United States)] (ORCID:0000000328428474)

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Abstract

This document is the final technical report for the UC Davis project DE-SC00023500, detailing the development and implementation of a Fast Recurrent Neural Network (FRNN) software. The software was designed to enable predictive, real-time control of plasma instabilities within the DIII-D Plasma Control System (PCS).

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BibTeX RIS

Luhmann, Neville [Univ. of California, Davis, CA (United States)] (ORCID:0000000206132931), Zhu, Yilun [Univ. of California, Davis, CA (United States)] (ORCID:0000000328428474). 2025-11-25. AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS) (Final Technical Report). https://doi.org/10.2172/3005512

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