DOE OSTI · 2999845
Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems
Abstract
Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.
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Shousha, R. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000314988980), Steiner, P. [Princeton Univ., NJ (United States)], Seo, J. [Chung-Ang Univ., Seoul (Korea, Republic of)], Conlin, R. [Univ. of Maryland, College Park, MD (United States)], Abbate, J. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States)], Xing, Z. A. [General Atomics, San Diego, CA (United States)], Kim, S. K. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000207018962), Erickson, K. G. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000312168719), Farre-Kaga, H. J. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States)], Rothstein, A. [Princeton Univ., NJ (United States)], Kolemen, E. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States)] (ORCID:0000000342123247), Majeski, R. [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000194370958). 2025-09-29. Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems. https://doi.org/10.1016/j.ifacol.2025.09.548
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