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RIS citation export for TUPA53: Modeling of Nonlinear Beam Dynamics via a Novel Particle-Mesh Method and Surrogate Models with Symplectic Neural Networks

TY  - CONF
AU  - Huang, C.-K.
AU  - Beznosov, O.
AU  - Burby, J.W.
AU  - Carlsten, B.E.
AU  - Dilts, G.A.
AU  - Domine, J.
AU  - Garimella, R.
AU  - Kim, A.
AU  - Kwan, T.J.
AU  - Li, F.Y.
AU  - Rakotoarivelo, H.N.
AU  - Robey, R.W.
AU  - Shen, B.
AU  - Tang, Q.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Modeling of Nonlinear Beam Dynamics via a Novel Particle-Mesh Method and Surrogate Models with Symplectic Neural Networks
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 462
EP  - 464
KW  - simulation
KW  - network
KW  - electron
KW  - radiation
KW  - synchrotron-radiation
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-TUPA53
UR  - https://jacow.org/napac2022/papers/tupa53.pdf
ER  -