Auralee L. Edelen
Auralee L. Edelen
SLAC National Accelerator Laboratory / Stanford
Verified email at - Homepage
Cited by
Cited by
Bayesian optimization of a free-electron laser
J Duris, D Kennedy, A Hanuka, J Shtalenkova, A Edelen, P Baxevanis, ...
Physical review letters 124 (12), 124801, 2020
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems
A Edelen, N Neveu, M Frey, Y Huber, C Mayes, A Adelmann
Physical Review Accelerators and Beams 23 (4), 044601, 2020
Neural networks for modeling and control of particle accelerators
AL Edelen, SG Biedron, BE Chase, D Edstrom, SV Milton, P Stabile
IEEE Transactions on Nuclear Science 63 (2), 878-897, 2016
Machine learning-based longitudinal phase space prediction of particle accelerators
C Emma, A Edelen, MJ Hogan, B O’Shea, G White, V Yakimenko
Physical Review Accelerators and Beams 21 (11), 112802, 2018
Demonstration of model-independent control of the longitudinal phase space of electron beams in the linac-coherent light source with femtosecond resolution
A Scheinker, A Edelen, D Bohler, C Emma, A Lutman
Physical review letters 121 (4), 044801, 2018
Opportunities in machine learning for particle accelerators
A Edelen, C Mayes, D Bowring, D Ratner, A Adelmann, R Ischebeck, ...
arXiv preprint arXiv:1811.03172, 2018
Physics model-informed Gaussian process for online optimization of particle accelerators
A Hanuka, X Huang, J Shtalenkova, D Kennedy, A Edelen, Z Zhang, ...
Physical Review Accelerators and Beams 24 (7), 072802, 2021
Multiobjective Bayesian optimization for online accelerator tuning
R Roussel, A Hanuka, A Edelen
Physical Review Accelerators and Beams 24 (6), 062801, 2021
First steps toward incorporating image based diagnostics into particle accelerator control systems using convolutional neural networks
AL Edelen, SG Biedron, SV Milton, JP Edelen
arXiv preprint arXiv:1612.05662, 2016
C Demonstration Research and Development Plan
EA Nanni, M Breidenbach, C Vernieri, S Belomestnykh, P Bhat, ...
arXiv preprint arXiv:2203.09076, 2022
Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
R Roussel, JP Gonzalez-Aguilera, YK Kim, E Wisniewski, W Liu, P Piot, ...
Nature communications 12 (1), 5612, 2021
Using a neural network control policy for rapid switching between beam parameters in an FEL
AL Edelen, SV Milton, SG Biedron, JP Edelen, PJM van der Slot
Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2017
Phase space reconstruction from accelerator beam measurements using neural networks and differentiable simulations
R Roussel, A Edelen, C Mayes, D Ratner, JP Gonzalez-Aguilera, S Kim, ...
Physical Review Letters 130 (14), 145001, 2023
Uncertainty quantification for deep learning in particle accelerator applications
AA Mishra, A Edelen, A Hanuka, C Mayes
Physical Review Accelerators and Beams 24 (11), 114601, 2021
Online tuning and light source control using a physics-informed Gaussian process Adi
A Hanuka, J Duris, J Shtalenkova, D Kennedy, A Edelen, D Ratner, ...
arXiv preprint arXiv:1911.01538, 2019
Neural network model of the PXIE RFQ cooling system and resonant frequency response
AL Edelen, SG Biedron, SV Milton, D Bowring, BE Chase, JP Edelen, ...
arXiv preprint arXiv:1612.07237, 2016
Improving surrogate model accuracy for the LCLS-II injector frontend using convolutional neural networks and transfer learning
L Gupta, A Edelen, N Neveu, A Mishra, C Mayes, YK Kim
Machine Learning: Science and Technology 2 (4), 045025, 2021
Toward the end-to-end optimization of particle physics instruments with differentiable programming
T Dorigo, A Giammanco, P Vischia, M Aehle, M Bawaj, A Boldyrev, ...
Reviews in Physics, 100085, 2023
Differentiable preisach modeling for characterization and optimization of particle accelerator systems with hysteresis
R Roussel, A Edelen, D Ratner, K Dubey, JP Gonzalez-Aguilera, YK Kim, ...
Physical Review Letters 128 (20), 204801, 2022
Machine learning models for optimization and control of x-ray free electron lasers
A Edelen, N Neveu, C Mayes, C Emma, D Ratner
NeurIPS Machine Learning for the Physical Sciences Workshop, 2019
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