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

Дата
2022
Авторы
Wu, G. -Z.
Fang, Y.
Wang, Y. -Y.
Dai, C. -Q.
Kudryashov, N. A.
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Научные группы
Организационные подразделения
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Организационная единица
Институт лазерных и плазменных технологий
Стратегическая цель Института ЛаПлаз – стать ведущей научной школой и ядром развития инноваций по лазерным, плазменным, радиационным и ускорительным технологиям, с уникальными образовательными программами, востребованными на российском и мировом рынке образовательных услуг.
Выпуск журнала
Аннотация
© 2022 Elsevier LtdIn 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.
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Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint / Wu, G.-Z. [et al.] // Chaos, Solitons and Fractals. - 2022. - 159. - 10.1016/j.chaos.2022.112143
URI
https://www.doi.org/10.1016/j.chaos.2022.112143
 https://www.scopus.com/record/display.uri?eid=2-s2.0-85129222196&origin=resultslist
 http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000801857600004
 https://openrepository.mephi.ru/handle/123456789/29052
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