Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modified physics-informed neural network method based on the
  conservation law constraint and its prediction of optical solitons   Based on conservation laws as one of the important integrable properties of
nonlinear physical models, we design a modified physics-informed neural network
method based on the conservation law constraint. From a global perspective,
this method imposes physical constraints on the solution of nonlinear physical
models by introducing the conservation law into the mean square error of the
loss function to train the neural network. Using this method, we mainly study
the standard nonlinear Schr\"odinger equation and predict various data-driven
optical soliton solutions, including one-soliton, soliton molecules,
two-soliton interaction, and rogue wave. In addition, based on various exact
solutions, we use the modified physics-informed neural network method based on
the conservation law constraint to predict the dispersion and nonlinear
coefficients of the standard nonlinear Schr\"odinger equation. Compared with
the traditional physics-informed neural network method, the modified method can
significantly improve the calculation accuracy.

4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio