Deep learning neural networks for the third-order nonlinear Schrodinger equation: Solitons, breathers, and rogue waves

04/30/2021

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by   Zijian Zhou, et al.

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33

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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 the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of solitons.