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G. C. Pappas, D. Lu, M. Schram, and D. L. Vrabie, “Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators”, in Proc. 12th Int. Particle Accelerator Conf. (IPAC'21), Campinas, Brazil, May 2021, pp. 4303-4306. |
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D. L. Kafkes and M. Schram, “Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster”, in Proc. 12th Int. Particle Accelerator Conf. (IPAC'21), Campinas, Brazil, May 2021, pp. 2268-2271. |
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M. I. Radaideh et al., “Progress on Machine Learning for the SNS High Voltage Converter Modulators”, in Proc. North American Particle Accelerator Conf. (NAPAC'22), Albuquerque, NM, USA, Aug. 2022, pp. 715-718. |
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W. Blokland et al., “Online Machine Learning Version of the SNS Differential Beam Current Monitor”, presented at the 11th Int. Beam Instrumentation Conference (IBIC'22), Kraków, Poland, Sep. 2022, paper WEP05, unpublished. |
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Y. Gao et al., “Optimization of AGS bunch merging with reinforcement learning”, in Proc. 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper TUPS53, pp. 1782-1785. |
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W. Blokland and M. Schram, “Machine learning for improved accelerator and target reliability”, presented at the 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper THXD1, unpublished. |
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K. Rajput, M. Schram, W. Blokland, A. Zhukov, and S. Lin, “Towards continual machine learning for particle accelerators”, in Proc. 14th International Beam Instrumentation Conference (IBIC'25), Liverpool, UK, Sep. 2025, pp. 302-308. |
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A. Kasparian et al., “Machine learning at the Spallation Neutron Source accelerator and target”, in Proc. 6th North American Particle Accelerator Conference (NAPAC'25), Sacramento, California, USA, Aug. 2025, pp. 181-185. |
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T. Hellert et al., “Agentic AI as a Middle Layer for Accelerator Control: Multi-Facility Deployment and Early Results”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOP6324, this conference. |
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J.-L. Vay et al., “Overview of the US DOE Multi-Office particle Accelerator Team (MOAT) project”, presented at the 17th Int. Particle Accelerator Conf. (IPAC'26), Deauville, France, May 2026, paper MOV6302, this conference. |
