  Modeling of conservative systems with neural networks is an area of active
research. A popular approach is to use Hamiltonian neural networks (HNNs) which
rely on the assumptions that a conservative system is described with Hamilton's
equations of motion. Many recent works focus on improving the integration
schemes used when training HNNs. In this work, we propose to enhance HNNs with
an estimation of a continuous-time trajectory of the modeled system using an
additional neural network, called a deep hidden physics model in the
literature. We demonstrate that the proposed integration scheme works well for
HNNs, especially with low sampling rates, noisy and irregular observations.
