desy-ml/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"
This project provides practical examples for physicists and engineers working with particle accelerators. It demonstrates how to use high-speed, differentiable simulations to optimize accelerator parameters, identify system characteristics from measurements, and improve tuning performance. You can input accelerator data and desired beam properties to get optimized settings and better understand system behavior. This tool is for particle accelerator scientists and engineers who want to apply machine learning to improve accelerator operations and design.
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Use this if you are a particle accelerator physicist or engineer looking to apply machine learning techniques for tasks like optimizing beam parameters, tuning accelerator sections, or identifying system characteristics.
Not ideal if you are a theoretical physicist or machine learning researcher not directly involved in the operation or design of physical particle accelerators.
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Language
Jupyter Notebook
License
GPL-3.0
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Last pushed
Jul 03, 2025
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