Author: Garimella, R.
Paper Title Page
TUPA53 Modeling of Nonlinear Beam Dynamics via a Novel Particle-Mesh Method and Surrogate Models with Symplectic Neural Networks 462
  • C.-K. Huang, O. Beznosov, J.W. Burby, B.E. Carlsten, G.A. Dilts, J. Domine, R. Garimella, A. Kim, T.J. Kwan, H.N. Rakotoarivelo, R.W. Robey, B. Shen, Q. Tang
    LANL, Los Alamos, New Mexico, USA
  • F.Y. Li
    New Mexico Consortium, Los Alamos, USA
  Funding: Work supported by the LDRD program at Los Alamos National Laboratory and the ASCR SciML program of DOE.
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.
[1] C.-K. Huang et al., Nucl. Instruments Methods Phys. Res. Sect. A, vol. 1034, p. 166808, 2022.
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA53  
About • Received ※ 25 July 2022 — Revised ※ 03 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 11 August 2022
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