This repository contains a collection of demos accompanying the Cheetah high-speed, differentiable beam dynamics simulation Python package.
For more information, see the paper where these demos were first introduced: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations.
benchmark
: Various speed benchmarks for Cheetah and other simulation tools.bo_prior
: Example of using a differentiable Cheetah model as a prior for Bayesian optimisation on a particle accelerator to improve tuning performance.neural_network_space_charge_quad
: Implementation of a modular neural network surrogate model for high-speed computation of space charge effects through a quadrupole magnet.reinforcement_learning
: Data and plotting code for example tuning performed by a neural network policy trained with reinforcement learning using a Cheetah simulation environment. The full RL example can be found in Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training.system_identification
: Example of using Cheetah with gradient-based optimisation to identify the parameters of a particle accelerator model from noisy measurements.tuning
: Example of using Cheetah with gradient-based optimisation to tune a particle accelerator subsection to a desired working point.
Please cite the original paper that these demos were introduced in:
@article{kaiser2024cheetah,
title = {Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations},
author = {Kaiser, Jan and Xu, Chenran and Eichler, Annika and Santamaria Garcia, Andrea},
year = 2024,
month = {May},
journal = {Phys. Rev. Accel. Beams},
publisher = {American Physical Society},
volume = 27,
pages = {054601},
doi = {10.1103/PhysRevAccelBeams.27.054601},
url = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.27.054601},
issue = 5,
numpages = 17
}