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dc.contributor.authorJohnsen, Trygve
dc.contributor.authorPratihar, Rakhi
dc.contributor.authorVerdure, Hugues
dc.date.accessioned2022-11-21T13:18:30Z
dc.date.available2022-11-21T13:18:30Z
dc.date.issued2022-07-07
dc.description.abstractThe Helmholtz equation has been used for modeling the sound pressure field under a harmonic load. Computing harmonic sound pressure fields by means of solving Helmholtz equation can quickly become unfeasible if one wants to study many different geometries for ranges of frequencies. We propose a machine learning approach, namely a feedforward dense neural network, for computing the average sound pressure over a frequency range. The data are generated with finite elements, by numerically computing the response of the average sound pressure, by an eigenmode decomposition of the pressure. We analyze the accuracy of the approximation and determine how much training data is needed in order to reach a certain accuracy in the predictions of the average pressure response.en_US
dc.identifier.citationJohnsen, Pratihar, Verdure. Weight spectra of Gabidulin rank-metric codes and Betti numbers. São Paulo Journal of Mathematical Sciences. 2022en_US
dc.identifier.cristinIDFRIDAID 2057744
dc.identifier.doi10.1007/s40863-022-00314-y
dc.identifier.issn1982-6907
dc.identifier.issn2316-9028
dc.identifier.urihttps://hdl.handle.net/10037/27450
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalSão Paulo Journal of Mathematical Sciences
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleWeight spectra of Gabidulin rank-metric codes and Betti numbersen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)