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UID:submissions.pasc-conference.org_PASC24_sess183_pap134@linklings.com
SUMMARY:Synthesizing Particle-In-Cell Simulations through Learning and GPU
  Computing for Hybrid Particle Accelerator Beamlines
DESCRIPTION:Keynote\n\nRyan T. Sandberg, Remi Lehe, Chad E. Mitchell, Marc
 o Garten, Andrew Myers, Ji Qiang, Jean-Luc Vay, and Axel Huebl (Lawrence B
 erkeley National Laboratory)\n\nParticle accelerator modeling is an import
 ant field of research and development, essential to investigating, designi
 ng and operating some of the most complex scientific devices ever built. K
 inetic simulations of relativistic, charged particle beams and advanced pl
 asma accelerator elements are often performed with high-fidelity particle-
 in-cell simulations, some of which fill the largest GPU supercomputers. St
 art-to-end modeling of a particle accelerator includes many elements and i
 t is desirable to integrate and model advanced accelerator elements fast, 
 in effective models. Traditionally, analytical and reduced-physics models 
 fill this role. The vast data from high-fidelity simulations and power of 
 GPU-accelerated computation open a new opportunity to complement tradition
 al modeling without approximations: surrogate modeling through machine lea
 rning. In this paper, we implement, present and benchmark such a data-driv
 en workflow, synthesising a conventional-surrogate simulation for hybrid p
 article accelerator beamlines.\n\nSession Chair: Cristina Silvano (Politec
 nico di Milano)
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