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dc.contributor.authorTran, Duy Tan
dc.contributor.authorRobinson, Haakon
dc.contributor.authorRasheed, Adil
dc.contributor.authorSan, Omer
dc.contributor.authorKvamsdal, Trond
dc.date.accessioned2020-11-06T09:29:40Z
dc.date.available2020-11-06T09:29:40Z
dc.date.created2020-11-05T12:45:39Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.urihttps://hdl.handle.net/11250/2686679
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.publisherIOP Publishingen_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 Seriesen_US
dc.source.issue012029en_US
dc.identifier.doi10.1088/1742-6596/1669/1/012029
dc.identifier.cristin1845254
dc.description.localcodeContent from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI 10.1088/1742-6596/1669/1/012029. Published under licence by IOP Publishing Ltden_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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