A Bayesian Optimization-Assisted, High-Performance Simulator for Modeling RF Accelerator Cavities
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DescriptionRadio-frequency cavities are key components for high-energy particle accelerators, quantum computing, etc. Designing cavities comes along with many computational challenges such as multi-objective optimization, high performance computing (HPC) requirement for handling large-sized cavities etc. To be more precise, its multi-objective optimization requires an efficient 3D full-wave electromagnetic simulator. For which, we rely on the integral equation (IE) method and it requires fast solver with HPC and ML algorithms to search for resonance modes.
We propose an HPC-based fast direct matrix solver for IE, combined with hybrid optimization algorithms to attain an efficient simulator for accelerator cavity modeling. 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 optimizer Downhill-simplex methods to generate the trial frequency samples which successfully optimize the corresponding 1D objective function with multiple sharp minimums.
We propose an HPC-based fast direct matrix solver for IE, combined with hybrid optimization algorithms to attain an efficient simulator for accelerator cavity modeling. 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 optimizer Downhill-simplex methods to generate the trial frequency samples which successfully optimize the corresponding 1D objective function with multiple sharp minimums.
