lanl/clarm
Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning
This tool helps scientists working with particle accelerators understand and predict the behavior of charged particle beams. It takes in existing data on particle beam phase space and can then generate realistic new beam configurations or forecast how beams will evolve through accelerator modules. Physicists and accelerator operators can use this to explore beam dynamics.
No commits in the last 6 months.
Use this if you need to generate realistic simulations or predict the future states of charged particle beams within an accelerator.
Not ideal if your work does not involve spatiotemporal dynamics of high-dimensional physical systems like particle beams.
Stars
9
Forks
3
Language
Python
License
BSD-3-Clause
Category
Last pushed
Apr 30, 2024
Commits (30d)
0
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