Prediction of symmetric and asymmetric solitons and model parameters for nonlinear Schrödinger equations with competing nonlinearities

Jun-Hang Jiang, Zhi-Zeng Si, Nikolay A. Kudryashov, Chao-Qing Dai and Wei Liu

Chaos, Solitons & Fractals, 2024, vol. 186, issue C

Abstract: he modified physics-informed neural network method with the learning rate decay is used to study two types of non-integrable nonlinear Schrödinger equations with competing nonlinearities. Symmetric and asymmetric solitons of nonlinear Schrödinger equations with competing quadratic-cubic and cubic-quintic nonlinearities are respectively predicted. The predicted results are presented from multiple aspects such as evolution process, error and loss function, which shows that the modified physical information neural network can effectively predict dynamics of these symmetric and asymmetric solitons in two cases of competing nonlinearities. From the error graphs of numerical and predictive solutions, the error gradually increases and mainly concentrates on the peak parts as the transmission distance increases. By setting appropriate loss function and improving the learning rate, iteration times and other aspects, model parameters are predicted successfully, and the influence of different number of iterations, neurons and hidden layers on the prediction error is compared. These findings have the certain reference value for using machine learning to predict the dynamics of optical solitons for non-integrable models.

Keywords: NLSE; Competing nonlinearity; Physics-informed neural network; Learning rate decay (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924008361
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:186:y:2024:i:c:s0960077924008361

DOI: 10.1016/j.chaos.2024.115284

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. ().