Data-driven discoveries of B\"acklund transforms and soliton evolution equations via deep neural network learning schemes
Abstract
We introduce a deep neural network learning scheme to learn the B\"acklund transforms (BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven soliton equation discovery based on the known BTs, respectively. The first scheme takes advantage of some solution (or soliton equation) information to study the data-driven BT of sine-Gordon equation, and complex and real Miura transforms between the defocusing (focusing) mKdV equation and KdV equation, as well as the data-driven mKdV equation discovery via the Miura transforms. The second deep learning scheme uses the explicit/implicit BTs generating the higher-order solitons to train the data-driven discovery of mKdV and sine-Gordon equations, in which the high-order solution informations are more powerful for the enhanced leaning soliton equations with higher accurates.
Keywords
Cite
@article{arxiv.2111.09489,
title = {Data-driven discoveries of B\"acklund transforms and soliton evolution equations via deep neural network learning schemes},
author = {Zijian Zhou and Li Wang and Weifang Weng and Zhenya Yan},
journal= {arXiv preprint arXiv:2111.09489},
year = {2023}
}
Comments
25 pages, 12 figures