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URLhttps://doi.org/10.18429/JACoW-IPAC2026-MOP6335
TitleAI-Enabled Digital Twins and Optimization Workflows for Accelerator Control
Authors
  • M. Yadav, A. Seryi, B. Terzic, J. Bird, J. Delayen, K. Makino, K. Ahmed, L. van Riesen-Haupt, Q. Su, S. De Silva, S. Hossain, T. Griffin
    Old Dominion University
  • T. Satogata
    Thomas Jefferson National Accelerator Facility
AbstractWe propose to develop advanced ML models, such as physics informed neural network (PINN) based surrogate models, to accurately represent accelerator phase space transport. These surrogate models will enable precise diagnosis and prediction of beam phase space evolution along the beamline, facilitating real-time control and optimization. The developed models will be tested using the Upgraded Injector Test Facility (UITF) at Thomas Jefferson National Accelerator Facility (JLab), providing a pathway toward ML-driven enhanced diagnostics and beamline control in operational accelerator environments. The primary aim will be to facilitate this by developing machine learning models that outperform traditional simulations in speed and precision. We will build a virtual beamline, train a reinforcement learning (RL) controller across varied calibration scenarios, and then transfer it to the real machine. Beyond operation, fast and accurate models are also essential for design optimization workflows using machine learning methods that iterate through design parameters. A long-term goal of this work will be to establish such workflows and apply them to the design of a compact accelerator at Old Dominion University (ODU).
Paperdownload: MOP6335.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference17th International Particle Accelerator Conference
Series
LocationDeauville, France
Date17-22 May 2026
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardEditorial Board
Online ISBN978-3-95450-252-3
Online ISSN2673-5350
Received12 May 2026
Revised25 June 2026
Accepted
Issued20 July 2026
DOI10.18429/JACoW-IPAC2026-MOP6335
Pages444-447