Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/113956
DC FieldValueLanguage
dc.contributorDepartment of Applied Mathematics-
dc.creatorCui, J-
dc.date.accessioned2025-07-04T08:34:15Z-
dc.date.available2025-07-04T08:34:15Z-
dc.identifier.issn0022-0396-
dc.identifier.urihttp://hdl.handle.net/10397/113956-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.subjectDivergenceen_US
dc.subjectExplicit numerical approximationen_US
dc.subjectRegularity estimateen_US
dc.subjectStochastic nonlinear Schrödinger equationen_US
dc.subjectStrong convergenceen_US
dc.titleExplicit approximation for stochastic nonlinear Schrödinger equationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage39-
dc.identifier.volume419-
dc.identifier.doi10.1016/j.jde.2024.11.022-
dcterms.abstractIn this paper, we study explicit approximations of stochastic nonlinear Schrödinger equations (SNLSEs). We first prove that the classical explicit numerical approximations are divergent for SNLSEs with polynomial nonlinearities. To enhance the stability, we propose a kind of explicit numerical approximations, and establish the regularity analysis and strong convergence rate of the proposed approximations for SNLSEs. There are two key ingredients in our approach. One ingredient is constructing a logarithmic auxiliary functional and exploiting the Bourgain space to prove new regularity estimates of SNLSEs. Another one is providing a dedicated error decomposition formula and presenting the tail estimates of underlying stochastic processes. In particular, our result answers the strong convergence problem of numerical approximation for 2D SNLSEs.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationJournal of differential equations, 25 Feb. 2025, v. 419, p. 1-39-
dcterms.isPartOfJournal of differential equations-
dcterms.issued2025-02-
dc.identifier.scopus2-s2.0-85209669219-
dc.identifier.eissn1090-2732-
dc.description.validate202507 bcch-
dc.identifier.FolderNumbera3799ben_US
dc.identifier.SubFormID51135en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2027-02-01en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2027-02-01
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