Paper  Title  Page 

TUZE3  Optimizing the Discovery of Underlying Nonlinear Beam Dynamics  335 


Funding: Work supported by US DOEHEP grants: DESC0010301 and DESC0022009 One of the DOEHEP Grand Challenges identified by Nagaitsev et al. relates to the use of virtual particle accelerators for beam prediction and optimization. Useful virtual accelerators rely on efficient and effective methodologies grounded in theory, simulation, and experiment. This paper uses an algorithm called Sparse Identification of Nonlinear Dynamical systems (SINDy), which has not previously been applied to beam physics. We believe the SINDy methodology promises to simplify the optimization of accelerator design and commissioning, particularly where space charge is important. We show how SINDy can be used to discover and identify the underlying differential equation system governing the beam moment evolution. We compare discovered differential equations to theoretical predictions and results from the PIC code WARP modeling. We then integrate the discovered differential system forward in time and compare the results to data analyzed in prior work using a Machine Learning paradigm called Reservoir Computing. Finally, we propose extending our methodology, SINDy for Virtual Accelerators (SINDyVA), to the broader community’s computational and real experiments. 

Slides TUZE3 [3.141 MB]  
DOI •  reference for this paper ※ doi:10.18429/JACoWNAPAC2022TUZE3  
About •  Received ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 22 August 2022  
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