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cs.LG2024
Learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier features
Torbjørn Smith, Olav Egeland
This paper presents a new method for learning dissipative Hamiltonian dynamics from a limited and noisy dataset. The method uses the Helmholtz decomposition to learn a vector field…
cs.LG2024★ 4 cited
Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
Torbjørn Smith, Olav Egeland
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) o…