mahindrautela/CLARM
Conditional Latent Autoregressive Recurrent Model (CLARM) for learning spatiotemporal dynamics
This project helps particle accelerator physicists understand and predict the behavior of charged particle beams. It takes data on the 6D phase space of beams within an accelerator and can either generate realistic new projections of the beam's phase space at different locations or forecast the beam's future states downstream. It's designed for researchers and engineers working with particle accelerators.
No commits in the last 6 months.
Use this if you need to simulate or predict the spatiotemporal evolution of charged particle beams in an accelerator, especially for forecasting or generating realistic beam dynamics.
Not ideal if your primary goal is real-time control or if you are working with spatiotemporal data outside the domain of particle physics.
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Last pushed
Sep 27, 2024
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