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@InProceedings{sun:napac2019-weple06,
author = {Y.P. Sun},
title = {{Linear and Second Order Map Tracking with Artificial Neural Network}},
booktitle = {Proc. NAPAC'19},
pages = {895--897},
paper = {WEPLE06},
language = {english},
keywords = {network, simulation, framework, software, storage-ring},
venue = {Lansing, MI, USA},
series = {North American Particle Accelerator Conference},
number = {4},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2019},
issn = {2673-7000},
isbn = {978-3-95450-223-3},
doi = {10.18429/JACoW-NAPAC2019-WEPLE06},
url = {http://jacow.org/napac2019/papers/weple06.pdf},
note = {https://doi.org/10.18429/JACoW-NAPAC2019-WEPLE06},
abstract = {In particle accelerators, the tracking simulation is usually performed with symplectic integration, or linear/nonlinear transfer maps. In this paper, it is shown that the linear/nonlinear transfer maps may be represented by an artificial neural network. To solve this multivariate regression problem, both random datasets and structured datasets are explored to train the neural networks. The achieved accuracy will be discussed.},
}