No AccessFlow Modal Decomposition and Deep Neural Networks for the Construction of Reduced Order Models of Compressible FlowsHugo Lui and William WolfHugo LuiUniversity of CampinasSearch for more papers by this author and William WolfUniversity of CampinasSearch for more papers by this authorAIAA 2019-1407Session: Novel CFD Methods IIIPublished Online:6 Jan 2019https://doi.org/10.2514/6.2019-1407SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookXLinked InRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetailsSee PDF for references What's Popular AIAA Scitech 2019 Forum 7-11 January 2019San Diego, Californiahttps://doi.org/10.2514/6.2019-1407 CrossmarkInformationCopyright © 2019 by The Authors. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. TopicsAerodynamicsAerospace SciencesComputational Fluid DynamicsConservation of Momentum EquationsEquations of Fluid DynamicsFinite Difference MethodFlow RegimesFluid DynamicsFluid Flow PropertiesFluid MechanicsHydraulicsHydrodynamicsNumerical AnalysisVortex Dynamics KeywordsIncompressible FlowFeedforward Neural NetworkRegression AnalysisKarman Vortex StreetFeature LearningNumerical SimulationLagrangian Coherent StructureTotal EnergyDrag ReductionSingular Value DecompositionDigital Topics Fluid Dynamics