Search Results for author:
Found 5 papers, 1 papers with code
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators
Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy.
Physics-constrained 3D Convolutional Neural Networks for Electrodynamics
We present a physics-constrained neural network (PCNN) approach to solving Maxwell's equations for the electromagnetic fields of intense relativistic charged particle beams.
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning
For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations.
Adaptive Latent Space Tuning for Non-Stationary Distributions
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.
