Author: Rakotoarivelo, H.N.
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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