Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by Generalized Kernel Interpolation (opens in new tab)

We extend the kernel-based symplectic predictor of [1] to a parameter-augmented setting in which the learned flow-map surrogate depends not only on the state, but also on additional variables such as physical parameters and macro time-step sizes. The method uses a product kernel ansatz on a parameter and macro step augmented domain and constructs the prediction through an implicit symplectic-Euler-type update. Hence, for every fixed admissible p...