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Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method

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dc.contributor.authorFang, Y.
dc.contributor.authorWu, G. Z.
dc.contributor.authorWang, Y. Y.
dc.contributor.authorDai, C. Q.
dc.contributor.authorKudryashov, N. A.
dc.contributor.authorКудряшов, Николай Алексеевич
dc.date.accessioned2024-12-25T13:43:28Z
dc.date.available2024-12-25T13:43:28Z
dc.date.issued2022
dc.description.abstract© 2022 Elsevier LtdIn the process of the deep learning, we integrate more integrable information of nonlinear wave models, such as the conservation law obtained from the integrable theory, into the neural network structure, and propose a conservation-law constrained neural network method with the flexible learning rate to predict solutions and parameters of nonlinear wave models. As some examples, we study real and complex typical nonlinear wave models, including nonlinear Schrödinger equation, Korteweg-de Vries and modified Korteweg-de Vries equations. Compared with the traditional physics-informed neural network method, this new method can more accurately predict solutions and parameters of some specific nonlinear wave models even when less information is needed, for example, in the absence of the boundary conditions. This provides a reference to further study solutions of nonlinear wave models by combining the deep learning and the integrable theory.
dc.identifier.citationData-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method / Fang, Y. [et al.] // Chaos, Solitons and Fractals. - 2022. - 158. - 10.1016/j.chaos.2022.112118
dc.identifier.doi10.1016/j.chaos.2022.112118
dc.identifier.urihttps://www.doi.org/10.1016/j.chaos.2022.112118
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85129196777&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000830316700022
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/27893
dc.relation.ispartofChaos, Solitons and Fractals
dc.titleData-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method
dc.typeArticle
dspace.entity.typePublication
oaire.citation.volume158
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