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Reveal normal form structure for nonlinear map in accelerator beam physics with symplectic neural network
Long-term multi-turn tracking is required to study nonlinear phenomena in circular accelerators, such as resonances, chaotic motion, and the dynamic aperture. General-purpose neural networks do not preserve the symplectic structure of Hamiltonian dynamics, so their errors can grow over many turns. S
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