101 citations · 230 across the 11 of their papers we have counts for
Showing 2021Show all
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physics.acc-ph2021★ 7 cited
Simulations of Future Particle Accelerators: Issues and Mitigations
D. Sagan, M. Berz, N. M. Cook +15
The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the succ…
physics.acc-ph2021★ 3 cited
Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning
Lipi Gupta, Auralee Edelen, Nicole Neveu +3
Machine learning models of accelerator systems (`surrogate models') are able to provide fast, accurate predictions of accelerator physics phenomena. However, approaches to date typ…