Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint

Gang-Zhou Wu, Yin Fang, Nikolay A. Kudryashov, Yue-Yue Wang and Chao-Qing Dai

Chaos, Solitons & Fractals, 2022, vol. 159, issue C

Abstract: In this work, based on the original physics-informed neural networks, we propose an improved physics-informed neural network method by combining the conservation laws. As one of the important integrable properties of nonlinear physical models, the conservation law can bring strong constraining force for the neural network to solve nonlinear physical models. Using this method, we study the standard nonlinear Schrödinger equation and predict various data-driven optical soliton solutions, including one-soliton, soliton molecules, two-soliton interaction, and rogue wave. In addition, from various exact solutions, we use the improved physics-informed neural network method to predict the dispersion and nonlinearity coefficients of the standard nonlinear Schrödinger equation based on the conservation law constraint. It turns out that the proposed method gives rise to the better results compared with the traditional physics-informed neural network method, and thus this method paves a way to simulate other physical models.

Keywords: Conservation laws; Improved physics-informed neural network; Standard nonlinear Schrödinger equation; Optical solitons (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922003538
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:159:y:2022:i:c:s0960077922003538

DOI: 10.1016/j.chaos.2022.112143

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().