IPPL is a C++ library to develop performance portable code for fully Eulerian, Lagrangian or hybrid Eulerian-Lagrangian methods.

Python 3 codes for beam optics measurements and corrections in circular particle accelerators

Code for the paper "Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning"

Python version of Simulated Commissioning toolkit for synchrotrons (https://github.com/ThorstenHellert/SC).

Additional tools for particle accelerator data analysis and machine information

Calculate various beam optics functions from TfsDataframes

I/O functionality for turn-by-turn BPM measurements data from different particle accelerators

MAD-X Example Study Scripts (MESS), for your everyday use

FPGA embedded multicore processing of an iterative deconvolution method based on sparse data representation aimed at online reconstruction of energy in particle accelerators

A python framework for particle accelerator simulations

Links in the endnotes of "Beams - The Story of Particle Accelerators and the Science They Discover" published by Springer