Fig. 3
From: Data-driven approach to the deep learning of the dynamics of a non-integrable Hamiltonian system
Architecture of the Long Short-Term Memory (LSTM) network for sequence data processing. The model consists of two stacked LSTM layers with 128 hidden units that process input sequences with two features. The output from the LSTM layers is then passed through a fully connected layer, which maps the features to a single output value. This output represents the predicted value of the chaoticity parameter k, based on the learned temporal dependencies in the sequential input data.
