Fig. 6
From: Data-driven approach to the deep learning of the dynamics of a non-integrable Hamiltonian system
Probability Density Functions (PDFs) of the logarithmic error \(\log (k_{\text {pred}}/k_{\text {true}})\), estimated using KDE with a Gaussian kernel, for three different values of k. The curves in each plot represent predictions from models trained on different numbers of trajectories for a fixed pair (k, L). This illustrates the effect of the number of trajectories on the accuracy of the predicted values for the chaoticity parameter k.
