Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114185
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dc.contributorDepartment of Applied Mathematics-
dc.creatorCai, Y-
dc.creatorChen, G-
dc.creatorQiao, Z-
dc.date.accessioned2025-07-15T08:44:05Z-
dc.date.available2025-07-15T08:44:05Z-
dc.identifier.issn1064-8275-
dc.identifier.urihttp://hdl.handle.net/10397/114185-
dc.language.isoenen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.rights© 2024 Society for Industrial and Applied Mathematicsen_US
dc.rightsCopyright © by SIAM. Unauthorized reproduction of this article is prohibited.en_US
dc.rightsThe following publication Hou, D., Ju, L., & Qiao, Z. (2024). Energy-Dissipative Spectral Renormalization Exponential Integrator Method for Gradient Flow Problems. SIAM Journal on Scientific Computing, 46(6), A3477-A3502 is available at https://doi.org/10.1137/23M158190X.en_US
dc.subjectEnergy dissipationen_US
dc.subjectExponential integratoren_US
dc.subjectGradient flowsen_US
dc.subjectSpectral renormalizationen_US
dc.subjectTime-steppingen_US
dc.titleEnergy-dissipative spectral renormalization exponential integrator method for gradient flow problemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spageA3477-
dc.identifier.epageA3502-
dc.identifier.volume46-
dc.identifier.issue6-
dc.identifier.doi10.1137/23M158190X-
dcterms.abstractIn this paper, we present a novel spectral renormalization exponential integrator method for solving gradient flow problems. Our method is specifically designed to simultaneously satisfy discrete analogues of the energy-dissipation laws and achieve high-order accuracy in time. To accomplish this, our method first incorporates the energy-dissipation law into the target gradient flow equation by introducing a time-dependent spectral renormalization (TDSR) factor. Then, the coupled equations are discretized using the spectral approximation in space and the exponential time differencing in time. Finally, the resulting fully discrete nonlinear system is decoupled and solved using the Picard iteration at each time step. Furthermore, we introduce an extra enforcing term into the system for updating the TDSR factor, which greatly relaxes the time-step size restriction of the proposed method and enhances its computational efficiency. Extensive numerical tests with various gradient flows are also presented to demonstrate the accuracy and effectiveness of our method as well as its high efficiency when combined with an adaptive time-stepping strategy for long-term simulations.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSIAM journal on scientific computing, 2024, v. 46, no. 6, p. A3477-3502-
dcterms.isPartOfSIAM journal on scientific computing-
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85209681796-
dc.identifier.eissn1095-7197-
dc.description.validate202507 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera3885ben_US
dc.identifier.SubFormID51544en_US
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryVoR alloweden_US
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