Document Type

Conference Paper

Publication Date

2026

DOI

10.18429/JACoW-IPAC2026-MOP6335

Publication Title

Proceedings of the 17th International Particle Accelerator Conference

Pages

444-447

Conference Name

17th International Particle Accelerator Conference, 17-22 May 2025, Deauville, France

Abstract

We 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).

Rights

Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Any further distribution of this work must maintain attribution to the authors, the published article's title, publisher, and DOI.

Comments

The DOI of this article, https://doi.org/10.18429/JACoW-IPAC2026-MOP6335, is non-functional as of 8/3/2026. This may change as the publisher develops a landing page.

Original Publication Citation

Yadav, M., Seryi, A., Terzic, B., Bird, J., Delayen, J., Makino, K., Ahmed, K., Riesen-Haupt, L. v., Su, Q., De Silva, S., Hossain, S., Griffin, T., & Satogata, T. (2026). AI-enabled digital twins and optimization workflows for accelerator control. In Proceedings of the 17th International Particle Accelerator Conference (pp. 444-447). JACoW Publishing. https://www.ipac26.org/prepress/doi/jacow-ipac2026-mop6335/index.html

ORCID

0000-0002-9646-8155 (Terzic), 0000-0002-8222-8740 (Delayen), 0000-0002-4809-9439 (De Silva), 0000-0002-8081-3815 (Satogata)

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