Fig. 5
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 Kernel Density Estimation (KDE) with a Gaussian kernel, for three different values of the chaoticity parameter k. Each plot shows the distributions of predictions from models trained on trajectories of varying lengths for a fixed pair (k, N). This demonstrates how sequence length influences the accuracy of predicting the parameter k.
