Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

It contains the nonparametric variant, in which the surrogate model is trained for one fixed Hamiltonian system and one prescribed macro time step ΔT; separate surrogate models are trained for different values of ΔT. Contents Training data Computed trajectories Reference solutions Fitted surrogate models Error quantities The data were generated by the provided Python scripts from Hamiltonian test systems and are organized according to the corresponding numerical experiments.

The dataset can be used to rerun the experiments, reproduce the figures, and further test structure-preserving kernel surrogate methods. How to run Experiment scripts are in the main folder SymplecticKernelApprox, named test*.py Run from a terminal: python <script_name>.py, or start from an IDE (Spyder, PyCharm, VS Code) Supporting functions are in the functions/ subfolder Stored and generated data are in the data/ folder See the README for more information and installation instructions.