JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{huang:napac2022-tupa53,
author = {C.-K. Huang and O. Beznosov and J.W. Burby and B.E. Carlsten and G.A. Dilts and J. Domine and R. Garimella and A. Kim and T.J. Kwan and F.Y. Li and H.N. Rakotoarivelo and R.W. Robey and B. Shen and Q. Tang},
% author = {C.-K. Huang and O. Beznosov and J.W. Burby and B.E. Carlsten and G.A. Dilts and J. Domine and others},
% author = {C.-K. Huang and others},
title = {{Modeling of Nonlinear Beam Dynamics via a Novel Particle-Mesh Method and Surrogate Models with Symplectic Neural Networks}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {462--464},
eid = {TUPA53},
language = {english},
keywords = {simulation, network, electron, radiation, synchrotron-radiation},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-TUPA53},
url = {https://jacow.org/napac2022/papers/tupa53.pdf},
abstract = {{The self-consistent nonlinear dynamics of a relativistic charged particle beam, particularly through the interaction with its complete self-fields, is a fundamental problem underpinning many accelerator design issues in high brightness beam applications, as well as the development of advanced accelerators. A novel self-consistent particle-mesh code, CoSyR [1], is developed based on a Lagrangian method for the calculation of the beam particles’ radiation near-fields and associated beam dynamics. Our recent simulations reveal the slice emittance growth in a bend and complex interplay between the longitudinal and transverse dynamics that are not captured in the 1D longitudinal static-state Coherent Synchrotron Radiation (CSR) model. We further show that surrogate models with symplectic neural networks can be trained from simulation data with significant time-savings for the modeling of nonlinear beam dynamics effects. Possibility to extend such surrogate models for the study of spin-orbital coupling is also briefly discussed.}},
}