Mostrar el registro sencillo del ítem

Model order reduction by convex displacement interpolation

hal.structure.identifierOptimad engineering [Torino]
dc.contributor.authorCUCCHIARA, Simona
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorIOLLO, Angelo
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorTADDEI, Tommaso
hal.structure.identifierOptimad engineering [Torino]
dc.contributor.authorTELIB, Haysam
dc.date.accessioned2024-04-04T02:31:32Z
dc.date.available2024-04-04T02:31:32Z
dc.date.issued2023-10-06
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190317
dc.description.abstractEnWe present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several snapshots. Given a library of high-fidelity simulations, we rely on a scalar testing function and on a point set registration method to identify coherent structures of the solution field in the form of sorted point clouds. Given a new parameter value, we exploit a regression method to predict the new point cloud; then, we resort to a boundary-aware registration technique to define bijective mappings that deform the new point cloud into the point clouds of the neighboring elements of the dataset, while preserving the boundary of the domain; finally, we define the estimate as a weighted combination of modes obtained by composing the neighboring snapshots with the previously-built mappings. We present several numerical examples for compressible and incompressible, viscous and inviscid flows to demonstrate the accuracy of the method. Furthermore, we employ the nonlinear interpolation procedure to augment the dataset of simulations for linear-subspace projection-based model reduction: our data augmentation procedure is designed to reduce offline costs -- which are dominated by snapshot generation -- of model reduction techniques for nonlinear advection-dominated problems.
dc.language.isoen
dc.subject.enmodel order reduction
dc.subject.ennonlinear approximations
dc.title.enModel order reduction by convex displacement interpolation
dc.typeDocument de travail - Pré-publication
dc.typePrepublication/Preprint
dc.subject.halMathématiques [math]
dc.subject.halPhysique [physics]
dc.identifier.arxiv2310.04290
dc.description.sponsorshipEuropeAccurate Roms for Industrial Applications
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-04375667
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04375667v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-10-06&rft.au=CUCCHIARA,%20Simona&IOLLO,%20Angelo&TADDEI,%20Tommaso&TELIB,%20Haysam&rft.genre=preprint&unknown


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem