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@inproceedings{frank:ipac2021-tupab215,
author = {J. Frank and M. Arlandoo and P. Goslawski and J. Li and T. Mertens and M. Ries and L. Vera Ramirez},
% author = {J. Frank and M. Arlandoo and P. Goslawski and J. Li and T. Mertens and M. Ries and others},
% author = {J. Frank and others},
title = {{Novel Non-Linear Particle Tracking Approach Employing Lie Algebraic Theory in the TensorFlow Environment}},
booktitle = {Proc. IPAC'21},
pages = {1920--1922},
eid = {TUPAB215},
language = {english},
keywords = {quadrupole, network, lattice, focusing, operation},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB215},
url = {https://jacow.org/ipac2021/papers/tupab215.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB215},
abstract = {{With this paper we present first results for encoding Lie transformations as computational graphs in Tensorflow that are used as layers in a neural network. By implementing a recursive differentiation scheme and employing Lie algebraic arguments we were able to reproduce the diagrams for well known lattice configurations. We track through simple optical lattices that are encountered as the main constituents of accelerators and demonstrate the flexibility and modularity our approach offers. The neural network can represent the optical lattice with predefined coefficients allowing for particle tracking for beam dynamics or can learn from experimental data to fine-tune beam optics.}},
}