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UID:submissions.supercomputing.org_SC22_sess273_rpost164@linklings.com
SUMMARY:A Bayesian Optimization-Assisted, High-Performance Simulator for M
odeling RF Accelerator Cavities
DESCRIPTION:Posters, Research Posters\n\nA Bayesian Optimization-Assisted,
High-Performance Simulator for Modeling RF Accelerator Cavities\n\nRani,
Liu, Luo, Luo, Li\n\nRadio-frequency cavities are key components for high-
energy particle accelerators, quantum computing, etc. Designing cavities c
omes along with many computational challenges such as multi-objective opti
mization, high performance computing (HPC) requirement for handling large-
sized cavities etc. To be more precise, its multi-objective optimization r
equires an efficient 3D full-wave electromagnetic simulator. For which, we
rely on the integral equation (IE) method and it requires fast solver wit
h HPC and ML algorithms to search for resonance modes. \n\nWe propose an H
PC-based fast direct matrix solver for IE, combined with hybrid optimizati
on algorithms to attain an efficient simulator for accelerator cavity mode
ling. First, we solve the linear eigen problem for each trial frequency by
a distributed-memory parallel, fast direct solver. Second, we propose the
combination of the global optimizer Gaussian Process with the local optim
izer Downhill-simplex methods to generate the trial frequency samples whic
h successfully optimize the corresponding 1D objective function with multi
ple sharp minimums.\n\nRegistration Category: Tech Program Reg Pass, Exhib
its Reg Pass
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