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Scientific Reports|August 5, 2024
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorsMahindra Rautela, Alan Williams, Alexander ScheinkerPhysical Review. E|March 19, 2025
Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversalMahindra Rautela, Alan Williams, Alexander ScheinkerUltrasonics|May 8, 2021
Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learningMahindra Rautela, J Senthilnath, Jochen Moll, et al.Scientific Reports|November 26, 2024
cDVAE: VAE-guided diffusion for particle accelerator beam 6D phase space projection diagnosticsAlexander ScheinkerScientific Reports|August 19, 2024
Conditional guided generative diffusion for particle accelerator beam diagnosticsAlexander ScheinkerScientific Reports|June 26, 2024
Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell's equationsChristopher Leon, Alexander ScheinkerThe Review of Scientific Instruments|April 21, 2026
4D beam matrix reconstruction in particle acceleratorsNikolai Yampolsky, Petr Anisimov, Alexander ScheinkerPhysical Review. E|May 18, 2023
Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact acceleratorAlexander Scheinker, Frederick Cropp, Daniele FilippettoScientific Reports|September 29, 2021
An adaptive approach to machine learning for compact particle acceleratorsAlexander Scheinker, Frederick Cropp, Sergio Paiagua, et al.Physical Review Letters|August 11, 2018
Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond ResolutionAlexander Scheinker, Auralee Edelen, Dorian Bohler, et al.Pageof 11