  In this paper we explore the performance of deep hidden physics model (M.
Raissi 2018) for autonomous systems. These systems are described by set of
ordinary differential equations which do not explicitly depend on time. Such
systems can be found in nature and have applications in modeling chemical
concentrations, population dynamics, n-body problems in physics etc. In this
work we consider dynamics of states, which explain how the states will evolve
are unknown to us. We approximate state and dynamics both using neural
networks. We have considered examples of 2D linear/nonlinear and Lorenz
systems. We observe that even without knowing all the states information, we
can estimate dynamics of certain states whose state information are known.
