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Y. Gao et al., “Optimization of AGS bunch merging with reinforcement learning”, presented at the 15th Int. Particle Accelerator Conf. (IPAC'24), Nashville, TN, USA, May 2024, paper TUPS53, this conference. |
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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 Beam Instrumentation Conference (IBIC'22), Kraków, Poland, Sep. 2022, paper WEP05, unpublished.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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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, this conference. |
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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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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. |
