  We use the physics-informed neural network to solve a variety of femtosecond
optical soliton solutions of the high order nonlinear Schr\"odinger equation,
including one-soliton solution, two-soliton solution, rogue wave solution,
W-soliton solution and M-soliton solution. The prediction error for
one-soliton, W-soliton and M-soliton is smaller. As the prediction distance
increases, the prediction error will gradually increase. The unknown physical
parameters of the high order nonlinear Schr\"odinger equation are studied by
using rogue wave solutions as data sets. The neural network is optimized from
three aspects including the number of layers of the neural network, the number
of neurons, and the sampling points. Compared with previous research, our error
is greatly reduced. This is not a replacement for the traditional numerical
method, but hopefully to open up new ideas.
