Publication:
An adaptive multiscale hybrid-mixed method for the Oseen equations

cris.sourceIdoai:repositorio.ucsc.cl:25022009/3066
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cris.virtualsource.author-orcide2ef3988-b021-4383-8cd4-7aa3e48b3184
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
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dc.contributor.authorAraya, Rodolfo
dc.contributor.authorCárcamo, Cristián
dc.contributor.authorDr. Poza-Díaz, Abner
dc.contributor.authorValentin, Frédéric
dc.date.accessioned2021-12-12T22:31:02Z
dc.date.accessioned2023-09-11T14:54:38Z
dc.date.available2021-12-12T22:31:02Z
dc.date.created2021-12-12T22:31:02Z
dc.date.issued2021
dc.description.abstractA novel residual a posteriori error estimator for the Oseen equations achieves efficiency and reliability by including multilevel contributions in its construction. Originates from the Multiscale Hybrid Mixed (MHM) method, the estimator combines residuals from the skeleton of the first-level partition of the domain, along with the contributions from element-wise approximations. The second-level estimator is local and infers the accuracy of multiscale basis computations as part of the MHM framework. Also, the face-degrees of freedom of the MHM method shape the estimator and induce a new face-adaptive procedure on the mesh’s skeleton only. As a result, the approach avoids re-meshing the first-level partition, which makes the adaptive process affordable and straightforward on complex geometries. Several numerical tests assess theoretical results.
dc.identifier.doi10.1007/s10444-020-09833-8
dc.identifier.urihttps://repositorio.ucsc.cl/handle/25022009/8487
dc.languageeng
dc.publisherSpringer Nature
dc.subjectA posteriori error estimator
dc.subjectOseen operator
dc.subjectMultiscale finite element
dc.subjectHigh-order approximation
dc.subject.ocdeCiencias Naturales::Matemáticas
dc.titleAn adaptive multiscale hybrid-mixed method for the Oseen equations
dc.typeartículo
dspace.entity.typePublication
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oairecerif.author.affiliationFacultad de Ingeniería
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relation.isAuthorOfPublicatione2ef3988-b021-4383-8cd4-7aa3e48b3184
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