Learning Hamiltonian Dynamics with Odd Symplectic Kernels and Random Features

A method is proposed to learn Hamiltonian vector fields on a reproducing kernel Hilbert space (RKHS) using an odd symplectic kernel. This ensures the learned vector fields are Hamiltonian and exhibit odd symmetry characteristics. Random Fourier features are used to approximate the proposed kernel, reducing the problem size.