cheetah-demos
Demos of Cheetah being used for various applications presented in "Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations"
Science Score: 49.0%
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Repository
Demos of Cheetah being used for various applications presented in "Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations"
Basic Info
- Host: GitHub
- Owner: desy-ml
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://doi.org/10.1103/PhysRevAccelBeams.27.054601
- Size: 149 MB
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- Stars: 6
- Watchers: 2
- Forks: 1
- Open Issues: 3
- Releases: 0
Topics
Metadata Files
README.md
Cheetah Demos
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.
Finding your way around
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.
Cite this repository
Please cite the original paper that these demos were introduced in:
bibtex
@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
}
Owner
- Name: desy-ml
- Login: desy-ml
- Kind: organization
- Location: Germany
- Repositories: 6
- Profile: https://github.com/desy-ml
GitHub Events
Total
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- Issue comment event: 3
- Push event: 11
- Pull request review event: 2
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Last Year
- Issues event: 3
- Watch event: 3
- Delete event: 1
- Issue comment event: 3
- Push event: 11
- Pull request review event: 2
- Pull request event: 2
- Fork event: 1
- Create event: 1