  We detail how incorporating physics into neural network design can
significantly improve the learning and forecasting of dynamical systems, even
nonlinear systems of many dimensions. A map building perspective elucidates the
superiority of Hamiltonian neural networks over conventional neural networks.
The results clarify the critical relation between data, dimension, and neural
network learning performance.
