Variational Multi-Scale Super-Resolution: A Data-Driven Approach for Reconstruction and Predictive Modeling of Unresolved Physics
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[1] Caskey,et al. GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS I . THE BASIC EXPERIMENT , 1962 .
[2] J. Smagorinsky,et al. GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS , 1963 .
[3] J. Ferziger,et al. Improved subgrid-scale models for large-eddy simulation , 1980 .
[4] T. Hughes,et al. The Galerkin/least-squares method for advective-diffusive equations , 1988 .
[5] P. Moin,et al. A dynamic subgrid‐scale eddy viscosity model , 1990 .
[6] T. Hughes,et al. Streamline upwind/Petrov-Galerkin formulations for convection dominated flows with particular emphasis on the incompressible Navier-Stokes equations , 1990 .
[7] D. Lilly,et al. A proposed modification of the Germano subgrid‐scale closure method , 1992 .
[8] F. Brezzi,et al. A relationship between stabilized finite element methods and the Galerkin method with bubble functions , 1992 .
[9] C. Farhat,et al. Bubble Functions Prompt Unusual Stabilized Finite Element Methods , 1994 .
[10] C. Meneveau,et al. On the properties of similarity subgrid-scale models as deduced from measurements in a turbulent jet , 1994, Journal of Fluid Mechanics.
[11] B. Geurts,et al. On the formulation of the dynamic mixed subgrid-scale model , 1994 .
[12] C. Meneveau,et al. A Lagrangian dynamic subgrid-scale model of turbulence , 1994, Journal of Fluid Mechanics.
[13] T. Lund,et al. Experiments with explicit filtering for LES using a finite-difference method , 1995 .
[14] T. Hughes. Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods , 1995 .
[15] C. Meneveau,et al. Experimental Study of Similarity Subgrid-Scale Models of Turbulence in the Far-Field of A Jet , 1995 .
[16] T. Hughes,et al. The variational multiscale method—a paradigm for computational mechanics , 1998 .
[17] N. Adams,et al. An approximate deconvolution procedure for large-eddy simulation , 1999 .
[18] F. Nicoud,et al. Subgrid-Scale Stress Modelling Based on the Square of the Velocity Gradient Tensor , 1999 .
[19] R. Moser,et al. Optimal LES formulations for isotropic turbulence , 1999, Journal of Fluid Mechanics.
[20] T. Hughes,et al. Large Eddy Simulation and the variational multiscale method , 2000 .
[21] S. Rebay,et al. GMRES Discontinuous Galerkin Solution of the Compressible Navier-Stokes Equations , 2000 .
[22] R. Codina. On stabilized finite element methods for linear systems of convection-diffusion-reaction equations , 2000 .
[23] N. Adams,et al. An approximate deconvolution model for large-eddy simulation with application to incompressible wall-bounded flows , 2001 .
[24] R. Codina. Stabilized finite element approximation of transient incompressible flows using orthogonal subscales , 2002 .
[25] T. Lund. The use of explicit filters in large eddy simulation , 2003 .
[26] F. Sarghini,et al. Neural networks based subgrid scale modeling in large eddy simulations , 2003 .
[27] A. W. Vreman. An eddy-viscosity subgrid-scale model for turbulent shear flow: Algebraic theory and applications , 2004 .
[28] T. Hughes,et al. Variational and Multiscale Methods in Turbulence , 2005 .
[29] Nikolaus A. Adams,et al. An adaptive local deconvolution method for implicit LES , 2005, J. Comput. Phys..
[30] Panagiotis Stinis,et al. Higher Order Mori-Zwanzig Models for the Euler Equations , 2006, Multiscale Model. Simul..
[31] R. Codina,et al. Time dependent subscales in the stabilized finite element approximation of incompressible flow problems , 2007 .
[32] P. Moin,et al. A dynamic global-coefficient subgrid-scale eddy-viscosity model for large-eddy simulation in complex geometries , 2007 .
[33] F. Toschi,et al. Shear-improved Smagorinsky model for large-eddy simulation of wall-bounded turbulent flows , 2006, Journal of Fluid Mechanics.
[34] T. Hughes,et al. Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows , 2007 .
[35] Parviz Moin,et al. Grid-independent large-eddy simulation using explicit filtering , 2008 .
[36] A. Oberai,et al. Spectral analysis of the dissipation of the residual-based variational multiscale method , 2010 .
[37] M. Kronbichler,et al. An algebraic variational multiscale-multigrid method for large eddy simulation of turbulent flow , 2010 .
[38] A. Oberai,et al. A mixed large eddy simulation model based on the residual-based variational multiscale formulation , 2010 .
[39] A. Masud,et al. A variational multiscale method for incompressible turbulent flows: Bubble functions and fine scale fields , 2011 .
[40] F. Nicoud,et al. Using singular values to build a subgrid-scale model for large eddy simulations , 2011 .
[41] J. Jiménez,et al. Effect of the computational domain on direct simulations of turbulent channels up to Reτ = 4200 , 2014 .
[42] J. Templeton,et al. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.
[43] K. Duraisamy,et al. Using field inversion to quantify functional errors in turbulence closures , 2016 .
[44] K. Duraisamy,et al. Non-Markovian Closure Models for Large Eddy Simulations using the Mori-Zwanzig Formalism , 2016, 1611.03311.
[45] Karthik Duraisamy,et al. A paradigm for data-driven predictive modeling using field inversion and machine learning , 2016, J. Comput. Phys..
[46] Yuji Hattori,et al. Searching for turbulence models by artificial neural network , 2016, 1607.01042.
[47] Gregor Gassner,et al. On the use of kinetic energy preserving DG-schemes for large eddy simulation , 2017, J. Comput. Phys..
[48] K. Duraisamy,et al. A Unified Framework for Multiscale Modeling using the Mori-Zwanzig Formalism and the Variational Multiscale Method , 2017, 1712.09669.
[49] Jinlong Wu,et al. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data , 2016, 1606.07987.
[50] Omer San,et al. A neural network approach for the blind deconvolution of turbulent flows , 2017, Journal of Fluid Mechanics.
[51] Spencer J. Sherwin,et al. On the eddy-resolving capability of high-order discontinuous Galerkin approaches to implicit LES / under-resolved DNS of Euler turbulence , 2017, J. Comput. Phys..
[52] J. Peraire,et al. Subgrid-scale modeling and implicit numerical dissipation in DG-based Large-Eddy Simulation , 2017 .
[53] Karthik Duraisamy,et al. A dynamic subgrid scale model for Large Eddy Simulations based on the Mori-Zwanzig formalism , 2016, J. Comput. Phys..
[54] Nils Thuerey,et al. tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow , 2018 .
[55] M. Mohebujjaman,et al. Physically constrained data‐driven correction for reduced‐order modeling of fluid flows , 2018, International Journal for Numerical Methods in Fluids.
[56] Jianren Fan,et al. Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation , 2018, Physics of Fluids.
[57] Traian Iliescu,et al. Data-Driven Filtered Reduced Order Modeling of Fluid Flows , 2017, SIAM J. Sci. Comput..
[58] Prakash Vedula,et al. Data-driven deconvolution for large eddy simulations of Kraichnan turbulence , 2018, Physics of Fluids.
[59] Guangrui Sun,et al. Implicit LES using adaptive filtering , 2017, J. Comput. Phys..
[60] Jonathan R. Holland,et al. Field Inversion and Machine Learning With Embedded Neural Networks: Physics-Consistent Neural Network Training , 2019, AIAA Aviation 2019 Forum.
[61] C. Xie,et al. Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence , 2019, Physics of Fluids.
[62] Heng Xiao,et al. Predictive large-eddy-simulation wall modeling via physics-informed neural networks , 2019, Physical Review Fluids.
[63] K. Taira,et al. Super-resolution reconstruction of turbulent flows with machine learning , 2018, Journal of Fluid Mechanics.
[64] Yingzheng Liu,et al. Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework , 2019 .
[65] Jamey D. Jacob,et al. Sub-grid scale model classification and blending through deep learning , 2018, Journal of Fluid Mechanics.
[66] Chao Ma,et al. Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network , 2019, Physical Review Fluids.
[67] Xi-yun Lu,et al. Deep learning methods for super-resolution reconstruction of turbulent flows , 2020, Physics of Fluids.
[68] K. Duraisamy,et al. Variational multiscale closures for finite element discretizations using the Mori–Zwanzig approach , 2019, 1906.01411.
[69] Qian Wang,et al. Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism , 2020, J. Comput. Phys..
[70] Kai Fukami,et al. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows , 2020, Journal of Fluid Mechanics.
[71] Changhong Mou,et al. Data-Driven Variational Multiscale Reduced Order Models , 2020, ArXiv.
[72] Karthik Duraisamy,et al. Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence , 2020, Physical Review Fluids.
[73] W. E,et al. Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence , 2020 .
[74] Embedded training of neural-network sub-grid-scale turbulence models , 2021, Physical Review Fluids.
[75] Hyojin Kim,et al. Unsupervised deep learning for super-resolution reconstruction of turbulence , 2020, Journal of Fluid Mechanics.