7 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning
Alexander Scheinker
Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of image…
Adaptive Latent Space Tuning for Non-Stationary Distributions
Alexander Scheinker, Frederick Cropp, Sergio Paiagua +1
Powerful deep learning tools, such as convolutional neural networks (CNN), are able to learn the input-output relationships of large complicated systems directly from data. Encoder…
Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators
Alexander Scheinker, Frederick Cropp, Sergio Paiagua +1
Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems d…