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dc.contributor.authorTran, Duy Tan
dc.contributor.authorRobinson, Haakon
dc.contributor.authorRasheed, Adil
dc.contributor.authorSan, Omer
dc.contributor.authorTabib, Mandar
dc.contributor.authorKvamsdal, Trond
dc.date.accessioned2023-03-28T13:32:19Z
dc.date.available2023-03-28T13:32:19Z
dc.date.created2020-11-05T12:45:39Z
dc.date.issued2020
dc.identifier.citationJournal of Physics: Conference Series (JPCS). 2020, 1669, 012029.en_US
dc.identifier.issn1742-6588
dc.identifier.urihttps://hdl.handle.net/11250/3060771
dc.description.abstractAtmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose.en_US
dc.language.isoengen_US
dc.publisherIOPen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleGANs enabled super-resolution reconstruction of wind fielden_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume1669en_US
dc.source.journalJournal of Physics: Conference Series (JPCS)en_US
dc.identifier.doi10.1088/1742-6596/1669/1/012029
dc.identifier.cristin1845254
dc.relation.projectNorges forskningsråd: 268044en_US
dc.source.articlenumber012029en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal