  The third-order nonlinear Schrodinger equation (alias the Hirota equation) is
investigated via deep leaning neural networks, which describes the strongly
dispersive ion-acoustic wave in plasma and the wave propagation of ultrashort
light pulses in optical fibers, as well as broader-banded waves on deep water.
In this paper, we use the physics-informed neural networks (PINNs) deep
learning method to explore the data-driven solutions (e.g., soliton, breather,
and rogue waves) of the Hirota equation when the two types of the unperturbated
and unperturbated (a 2% noise) training data are considered. Moreover, we use
the PINNs deep learning to study the data-driven discovery of parameters
appearing in the Hirota equation with the aid of solitons.
