Fetching the paper…
Reading the bibliography…
We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex via a Field-Programmable Gate Array (FPGA).
N. Minorsky “Directional Stability of Automatically Steered Bodies”, J. Am. Soc. Nav. Engineers , vol. 34, no. 2, p. 280, 1922, 10.1111/j.1559-3584.1922.tb04958.x
1922
Earlier work this paper cites.
J.G. Ziegler and N.B. Nichols “Optimum Settings for Automatic Controllers”, Trans. ASME , vol. 64, p. 759, 1942,
1942
Earlier work this paper cites.
C. Granger "Investigating Causal Relations By Econometric Models and Cross-spectral methods”, Econometrica , vol. 37, p. 424, 1969, 10.2307/1912791
1969
Earlier work this paper cites.
J. Ryk, “Gradient Magnet Power Supply for the Fermilab 8-GeV Proton Synchrotron”, Fermilab, Batavia, IL, USA, Rep. FERMILAB-PUB-74-085, Aug. 1984
1984
Earlier work this paper cites.
J. Crawford et al , Booster Rookie Book Manual v4.1 , 2009, https://operations.fnal.gov/rookie_books/Booster_V4.1.pdf
2009
Earlier work this paper cites.
F. Pedregosa, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research , vol. 12, p. 2825, 2011,
2011
Earlier work this paper cites.
V. Mnih, et al. , “Playing Atari with Deep Reinforcement Learning”, in NIPS Deep Learning Workshop 2013 , Lake Tahoe, NV USA Dec. 2013
2013
Cited alongside, same era.
J. Gao, S. Haghighi, and D. Hatzinakos, “Reference empirical mode decomposition”, in 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE) , Toronto, CA, May. 2014
2014
Cited alongside, same era.
V. Mnih et al. “Human-level control through deep reinforcement learning”, Nature , vol. 518, p. 219, 2015, 10.1038/nature14236
2015
Cited alongside, same era.
A. Edelen et al. , “Neural Networks for Modeling and Control of Particle Accelerators”, IEEE Trans. Nucl. Sci. , vol. 63, no. 2, p. 878, Apr. 2016, doi:10.1109/TNS.2016.2543203
2016
Cited alongside, same era.
Y. Gal, J. Hron, and A. Kendall, "Concrete Dropout” in Advances in Neural Information Processing Systems 30 (NIPS 2017) , Long Beach, CA USA Dec. 2017
2017
Later among the works it cites.
R. Sutton and A. Barto, “Reinforcement Learning: An Introduction”, MIT Press Cambridge, MA, USA: 2018
2018
Later among the works it cites.
V. François-Lavet et al. “An Introduction to Deep Reinforcement Learning”, Found. Trends Mach. Learn. , vol. 11, p. 219, 2018, 10.1561/2200000071
2018
Later among the works it cites.
R. Keller, “Controlling Currents”, Fermilab, Batavia, IL USA, Aug. 2019
2019
Later among the works it cites.
J. Duris et al. , “Bayesian optimization of a free-electron laser”, Phys. Rev. Lett. , vol. 124, no. 12, p. 124801, 2020, doi:10.1103/PhysRevLett.124.124801
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
A. Edelen, et al. , “Using A Neural Network Control Policy For Rapid Switching Between Beam Parameters In An FEL”, in Proceedings of the 38th International Free-Electron Laser Conference , Santa Fe, NM, USA, Aug. 2017
2017
Cited alongside, same era.
J. St. John et al. , “Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster”, submitted for publication in Physical Review Accelerators and Beams
Cited in the paper.
D. Kafkes and J. St.John, “BOOSTR: A Dataset for Accelerator Control Systems”, MDPI Data , vol. 24, no. 6, p. 124801, 2021, doi:10.3390/data6040042
2021
Closest in time.