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dc.contributor.authorPawar, Suraj
dc.contributor.authorRahman, Sk. Mashfiqur
dc.contributor.authorVaddireddy, H
dc.contributor.authorSan, Omer
dc.contributor.authorRasheed, Adil
dc.contributor.authorVedula, Prakash
dc.date.accessioned2020-01-24T10:01:42Z
dc.date.available2020-01-24T10:01:42Z
dc.date.created2019-09-24T17:31:04Z
dc.date.issued2019
dc.identifier.issn1070-6631
dc.identifier.urihttp://hdl.handle.net/11250/2637771
dc.description.abstractIn this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly nonintrusive since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate and can be used as a robust predictive tool for nonintrusive model order reduction of complex fluid flows.nb_NO
dc.language.isoengnb_NO
dc.publisherAIP Publishingnb_NO
dc.titleA deep learning enabler for non-intrusive reduced order modeling of fluid flowsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.volume31nb_NO
dc.source.journalPhysics of Fluidsnb_NO
dc.source.issue085101nb_NO
dc.identifier.doi10.1063/1.5113494
dc.identifier.cristin1728536
dc.description.localcodeLocked until 1.8.2020 due to copyright restrictions. Published by AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in Journal of Applied Physics and may be found at http://dx.doi.org/10.1063/1.5113494nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


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