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Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
April 12, 2024, 4:41 a.m. | Torbj{\o}rn Smith, Olav Egeland
cs.LG updates on arXiv.org arxiv.org
Abstract: A method for learning Hamiltonian dynamics from a limited and noisy dataset is proposed. The method learns a Hamiltonian vector field on a reproducing kernel Hilbert space (RKHS) of inherently Hamiltonian vector fields, and in particular, odd Hamiltonian vector fields. This is done with a symplectic kernel, and it is shown how the kernel can be modified to an odd symplectic kernel to impose the odd symmetry. A random feature approximation is developed for the …
abstract arxiv cs.lg cs.ro cs.sy dataset dynamics eess.sy features fields kernel random space spaces type vector
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