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Machine Learning to Enable Orders of Magnitude Speedup in Mult-Objective Optimization of Particle Accelerator Systems

By Auralee Edelen and others
High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerator systems. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as online models tied directly to accelerator operation. We demonstrate... Show more
March 21, 2019
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Machine Learning to Enable Orders of Magnitude Speedup in Mult-Objective Optimization of Particle Accelerator Systems
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