A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

Pawar, Suraj; San, Omer; Rasheed, Adil; Vedula, Prakash

doi:10.1007/s00162-019-00512-z

Title: A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

Journal Article · 03 January 2020 · Theoretical and Computational Fluid Dynamics

DOI: https://doi.org/10.1007/s00162-019-00512-z · OSTI ID:1737499

Pawar, Suraj ^[1];

^[2]; Rasheed, Adil ^[3]; Vedula, Prakash ^[4]

Oklahoma State Univ., Stillwater, OK (United States); Oklahoma State University Stillwater
Oklahoma State Univ., Stillwater, OK (United States)
Norwegian Univ. of Science and Technology, Trondheim (Norway)
Univ. of Oklahoma, Norman, OK (United States)

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oklahoma State Univ., Stillwater, OK (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: SC0019290

OSTI ID:: 1737499

Journal Information:: Theoretical and Computational Fluid Dynamics, Journal Name: Theoretical and Computational Fluid Dynamics Journal Issue: 4 Vol. 34; ISSN 0935-4964

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (104)

Deep neural networks for data-driven LES closure models Beck, Andrea; Flad, David; Munz, Claus-Dieter Journal of Computational Physics, Vol. 398 https://doi.org/10.1016/j.jcp.2019.108910	journal	December 2019
The structure of isotropic turbulence at very high Reynolds numbers Kraichnan, Robert H. Journal of Fluid Mechanics, Vol. 5, Issue 04 https://doi.org/10.1017/s0022112059000362	journal	May 1959
On the magnitude of the subgrid-scale eddy coefficient in large-eddy simulations of turbulent channel flow Mason, P. J.; Callen, N. S. Journal of Fluid Mechanics, Vol. 162, Issue -1 https://doi.org/10.1017/s0022112086002112	journal	January 1986
A dynamic localization model for large-eddy simulation of turbulent flows Ghosal, S.; Lund, T. S.; Moin, P. Journal of Fluid Mechanics, Vol. 297 https://doi.org/10.1017/s0022112095003156	journal	August 1995
Computation of the Energy Spectrum in Homogeneous Two-Dimensional Turbulence Batchelor, G. K. Physics of Fluids, Vol. 12, Issue 12 https://doi.org/10.1063/1.1692443	journal	January 1969
Inertial Ranges in Two-Dimensional Turbulence Kraichnan, Robert H. Physics of Fluids, Vol. 10, Issue 7 https://doi.org/10.1063/1.1762301	journal	January 1967
Two-dimensional turbulence Kraichnan, R. H.; Montgomery, D. Reports on Progress in Physics, Vol. 43, Issue 5 https://doi.org/10.1088/0034-4885/43/5/001	journal	May 1980
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks Vlachas, Pantelis R.; Byeon, Wonmin; Wan, Zhong Y. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 474, Issue 2213 https://doi.org/10.1098/rspa.2017.0844	journal	May 2018
Atmospheric Predictability and Two-Dimensional Turbulence Leith, C. E. Journal of the Atmospheric Sciences, Vol. 28, Issue 2 https://doi.org/10.1175/1520-0469(1971)028<0145:apatdt>2.0.co;2	journal	March 1971
General Circulation Experiments with the Primitive Equations: i. the Basic Experiment* Smagorinsky, J. Monthly Weather Review, Vol. 91, Issue 3 https://doi.org/10.1175/1520-0493(1963)091<0099:gcewtp>2.3.co;2	journal	March 1963
Machine Learning Based Detection of Flow Disturbances Using Surface Pressure Measurements Hou, Wei; Darakananda, Darwin; Eldredge, Jeff AIAA Scitech 2019 Forum https://doi.org/10.2514/6.2019-1148	conference	January 2019
Modelling turbulent premixed flames using convolutional neural networks: application to sub-grid scale variance and filtered reaction rate Nikolaou, Zacharias M.; Chrysostomou, Charalambos; Vervisch, Luc arXiv https://doi.org/10.48550/arxiv.1810.07944	preprint	January 2018
Super-resolution reconstruction of turbulent flows with machine learning Fukami, Kai; Fukagata, Koji; Taira, Kunihiko arXiv https://doi.org/10.48550/arxiv.1811.11328	text	January 2018
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders Lee, Kookjin; Carlberg, Kevin arXiv https://doi.org/10.48550/arxiv.1812.08373	preprint	January 2018
Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction Erichson, N. Benjamin; Muehlebach, Michael; Mahoney, Michael W. arXiv https://doi.org/10.48550/arxiv.1905.10866	preprint	January 2019
Deep Learning parameterization of subgrid scales in wall-bounded turbulent flows Pal, Anikesh arXiv https://doi.org/10.48550/arxiv.1905.12765	preprint	January 2019
Physically constrained data‐driven correction for reduced‐order modeling of fluid flows Mohebujjaman, M.; Rebholz, L. G.; Iliescu, T. International Journal for Numerical Methods in Fluids, Vol. 89, Issue 3 https://doi.org/10.1002/fld.4684	journal	October 2018
Neural Network Modeling for Near Wall Turbulent Flow Milano, Michele; Koumoutsakos, Petros Journal of Computational Physics, Vol. 182, Issue 1 https://doi.org/10.1006/jcph.2002.7146	journal	October 2002
Near-wall turbulence closure modeling without “damping functions” Durbin, P. A. Theoretical and Computational Fluid Dynamics, Vol. 3, Issue 1 https://doi.org/10.1007/BF00271513	journal	September 1991
Experimental study of similarity subgrid-scale models of turbulence in the far-field of a jet Liu, Shewen; Meneveau, Charles; Katz, Joseph Applied Scientific Research, Vol. 54, Issue 3 https://doi.org/10.1007/BF00849115	journal	April 1995
Prediction of aerodynamic flow fields using convolutional neural networks Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu Computational Mechanics, Vol. 64, Issue 2 https://doi.org/10.1007/s00466-019-01740-0	journal	June 2019
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods Nikolaou, Z. M.; Chrysostomou, C.; Vervisch, L. Flow, Turbulence and Combustion, Vol. 103, Issue 2 https://doi.org/10.1007/s10494-019-00028-w	journal	April 2019
Neural networks based subgrid scale modeling in large eddy simulations Sarghini, F.; de Felice, G.; Santini, S. Computers & Fluids, Vol. 32, Issue 1 https://doi.org/10.1016/S0045-7930(01)00098-6	journal	January 2003
Two-dimensional turbulence: a physicist approach Tabeling, P. Physics Reports, Vol. 362, Issue 1 https://doi.org/10.1016/S0370-1573(01)00064-3	journal	May 2002
Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates Lapeyre, Corentin J.; Misdariis, Antony; Cazard, Nicolas Combustion and Flame, Vol. 203 https://doi.org/10.1016/j.combustflame.2019.02.019	journal	May 2019
High-order methods for decaying two-dimensional homogeneous isotropic turbulence San, Omer; Staples, Anne E. Computers & Fluids, Vol. 63 https://doi.org/10.1016/j.compfluid.2012.04.006	journal	June 2012
A stable and scale-aware dynamic modeling framework for subgrid-scale parameterizations of two-dimensional turbulence Maulik, Romit; San, Omer Computers & Fluids, Vol. 158 https://doi.org/10.1016/j.compfluid.2016.11.015	journal	November 2017
Hidden physics models: Machine learning of nonlinear partial differential equations Raissi, Maziar; Karniadakis, George Em Journal of Computational Physics, Vol. 357 https://doi.org/10.1016/j.jcp.2017.11.039	journal	March 2018
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Deep neural networks for data-driven LES closure models Beck, Andrea; Flad, David; Munz, Claus-Dieter Journal of Computational Physics, Vol. 398 https://doi.org/10.1016/j.jcp.2019.108910	journal	December 2019
Constraint-aware neural networks for Riemann problems Magiera, Jim; Ray, Deep; Hesthaven, Jan S. Journal of Computational Physics, Vol. 409 https://doi.org/10.1016/j.jcp.2020.109345	journal	May 2020
Turbulence: The Legacy of A. N. Kolmogorov Frisch, Uriel https://doi.org/10.1017/CBO9781139170666	book	January 1996
The structure of isotropic turbulence at very high Reynolds numbers Kraichnan, Robert H. Journal of Fluid Mechanics, Vol. 5, Issue 04 https://doi.org/10.1017/S0022112059000362	journal	May 1959
A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers Deardorff, James W. Journal of Fluid Mechanics, Vol. 41, Issue 2 https://doi.org/10.1017/S0022112070000691	journal	April 1970
Progress in the development of a Reynolds-stress turbulence closure Launder, B. E.; Reece, G. J.; Rodi, W. Journal of Fluid Mechanics, Vol. 68, Issue 3 https://doi.org/10.1017/S0022112075001814	journal	April 1975
A more general effective-viscosity hypothesis Pope, S. B. Journal of Fluid Mechanics, Vol. 72, Issue 02 https://doi.org/10.1017/S0022112075003382	journal	November 1975
On the magnitude of the subgrid-scale eddy coefficient in large-eddy simulations of turbulent channel flow Mason, P. J.; Callen, N. S. Journal of Fluid Mechanics, Vol. 162, Issue -1 https://doi.org/10.1017/S0022112086002112	journal	January 1986
Toward the large-eddy simulation of compressible turbulent flows Erlebacher, G.; Hussaini, M. Y.; Speziale, C. G. Journal of Fluid Mechanics, Vol. 238 https://doi.org/10.1017/S0022112092001678	journal	May 1992
A dynamic localization model for large-eddy simulation of turbulent flows Ghosal, Sandip; Lund, Thomas S.; Moin, Parviz Journal of Fluid Mechanics, Vol. 286 https://doi.org/10.1017/S0022112095000711	journal	March 1995
A Lagrangian dynamic subgrid-scale model of turbulence Meneveau, Charles; Lund, Thomas S.; Cabot, William H. Journal of Fluid Mechanics, Vol. 319, Issue -1 https://doi.org/10.1017/S0022112096007379	journal	July 1996
Large eddy simulations of stratified turbulence: the dynamic Smagorinsky model Khani, Sina; Waite, Michael L. Journal of Fluid Mechanics, Vol. 773 https://doi.org/10.1017/jfm.2015.249	journal	May 2015
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy Journal of Fluid Mechanics, Vol. 807 https://doi.org/10.1017/jfm.2016.615	journal	October 2016
Deep learning in fluid dynamics Kutz, J. Nathan Journal of Fluid Mechanics, Vol. 814 https://doi.org/10.1017/jfm.2016.803	journal	January 2017
A neural network approach for the blind deconvolution of turbulent flows Maulik, R.; San, O. Journal of Fluid Mechanics, Vol. 831 https://doi.org/10.1017/jfm.2017.637	journal	October 2017
Subgrid modelling for two-dimensional turbulence using neural networks Maulik, R.; San, O.; Rasheed, A. Journal of Fluid Mechanics, Vol. 858 https://doi.org/10.1017/jfm.2018.770	journal	November 2018
Super-resolution reconstruction of turbulent flows with machine learning Fukami, Kai; Fukagata, Koji; Taira, Kunihiko Journal of Fluid Mechanics, Vol. 870 https://doi.org/10.1017/jfm.2019.238	journal	May 2019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics Murata, Takaaki; Fukami, Kai; Fukagata, Koji Journal of Fluid Mechanics, Vol. 882 https://doi.org/10.1017/jfm.2019.822	journal	November 2019
A dynamic localization model for large-eddy simulation of turbulent flows Ghosal, S.; Lund, T. S.; Moin, P. Journal of Fluid Mechanics, Vol. 297 https://doi.org/10.1017/s0022112095003156	journal	August 1995
Numerical study of dynamic Smagorinsky models in large-eddy simulation of the atmospheric boundary layer: Validation in stable and unstable conditions: LES STUDY OF DYNAMIC SGS MODELS Kleissl, Jan; Kumar, Vijayant; Meneveau, Charles Water Resources Research, Vol. 42, Issue 6 https://doi.org/10.1029/2005WR004685	journal	June 2006
Development of a turbulence closure model for geophysical fluid problems Mellor, George L.; Yamada, Tetsuji Reviews of Geophysics, Vol. 20, Issue 4 https://doi.org/10.1029/RG020i004p00851	journal	January 1982
Numerical simulation of turbulent flows Fox, Douglas G.; Lilly, Douglas K. Reviews of Geophysics, Vol. 10, Issue 1 https://doi.org/10.1029/rg010i001p00051	journal	January 1972
Computation of the Energy Spectrum in Homogeneous Two-Dimensional Turbulence Batchelor, G. K. Physics of Fluids, Vol. 12, Issue 12 https://doi.org/10.1063/1.1692443	journal	January 1969
Inertial Ranges in Two-Dimensional Turbulence Kraichnan, Robert H. Physics of Fluids, Vol. 10, Issue 7 https://doi.org/10.1063/1.1762301	journal	January 1967
Reduction of the Germano-identity error in the dynamic Smagorinsky model Park, Noma; Mahesh, Krishnan Physics of Fluids, Vol. 21, Issue 6 https://doi.org/10.1063/1.3140033	journal	June 2009
Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation Wang, Zhuo; Luo, Kun; Li, Dong Physics of Fluids, Vol. 30, Issue 12 https://doi.org/10.1063/1.5054835	journal	December 2018
Machine learning methods for turbulence modeling in subsonic flows around airfoils Zhu, Linyang; Zhang, Weiwei; Kou, Jiaqing Physics of Fluids, Vol. 31, Issue 1 https://doi.org/10.1063/1.5061693	journal	January 2019
A deep learning enabler for nonintrusive reduced order modeling of fluid flows Pawar, S.; Rahman, S. M.; Vaddireddy, H. Physics of Fluids, Vol. 31, Issue 8 https://doi.org/10.1063/1.5113494	journal	August 2019
A dynamic subgrid‐scale eddy viscosity model Germano, Massimo; Piomelli, Ugo; Moin, Parviz Physics of Fluids A: Fluid Dynamics, Vol. 3, Issue 7 https://doi.org/10.1063/1.857955	journal	July 1991
A dynamic subgrid‐scale model for compressible turbulence and scalar transport Moin, P.; Squires, K.; Cabot, W. Physics of Fluids A: Fluid Dynamics, Vol. 3, Issue 11 https://doi.org/10.1063/1.858164	journal	November 1991
A proposed modification of the Germano subgrid‐scale closure method Lilly, D. K. Physics of Fluids A: Fluid Dynamics, Vol. 4, Issue 3 https://doi.org/10.1063/1.858280	journal	March 1992
Model consistency in large eddy simulation of turbulent channel flows Piomelli, Ugo; Moin, Parviz; Ferziger, Joel H. Physics of Fluids, Vol. 31, Issue 7 https://doi.org/10.1063/1.866635	journal	January 1988
A dynamic eddy-viscosity closure model for large eddy simulations of two-dimensional decaying turbulence San, O. International Journal of Computational Fluid Dynamics, Vol. 28, Issue 6-10 https://doi.org/10.1080/10618562.2014.948426	journal	June 2014
Two-dimensional turbulence Kraichnan, R. H.; Montgomery, D. Reports on Progress in Physics, Vol. 43, Issue 5 https://doi.org/10.1088/0034-4885/43/5/001	journal	May 1980
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks Vlachas, Pantelis R.; Byeon, Wonmin; Wan, Zhong Y. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 474, Issue 2213 https://doi.org/10.1098/rspa.2017.0844	journal	May 2018
Shallow neural networks for fluid flow reconstruction with limited sensors Erichson, N. Benjamin; Mathelin, Lionel; Yao, Zhewei Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 476, Issue 2238 https://doi.org/10.1098/rspa.2020.0097	journal	June 2020
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng Physical Review Fluids, Vol. 2, Issue 3 https://doi.org/10.1103/PhysRevFluids.2.034603	journal	March 2017
Searching for turbulence models by artificial neural network Gamahara, Masataka; Hattori, Yuji Physical Review Fluids, Vol. 2, Issue 5 https://doi.org/10.1103/PhysRevFluids.2.054604	journal	May 2017
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework Wu, Jin-Long; Xiao, Heng; Paterson, Eric Physical Review Fluids, Vol. 3, Issue 7 https://doi.org/10.1103/PhysRevFluids.3.074602	journal	July 2018
Predictive large-eddy-simulation wall modeling via physics-informed neural networks Yang, X. I. A.; Zafar, S.; Wang, J. -X. Physical Review Fluids, Vol. 4, Issue 3 https://doi.org/10.1103/PhysRevFluids.4.034602	journal	March 2019
Predictions of turbulent shear flows using deep neural networks Srinivasan, P. A.; Guastoni, L.; Azizpour, H. Physical Review Fluids, Vol. 4, Issue 5 https://doi.org/10.1103/PhysRevFluids.4.054603	journal	May 2019
Perspective on machine learning for advancing fluid mechanics Brenner, M. P.; Eldredge, J. D.; Freund, J. B. Physical Review Fluids, Vol. 4, Issue 10 https://doi.org/10.1103/PhysRevFluids.4.100501	journal	October 2019
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach Pathak, Jaideep; Hunt, Brian; Girvan, Michelle Physical Review Letters, Vol. 120, Issue 2 https://doi.org/10.1103/PhysRevLett.120.024102	journal	January 2018
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.182	conference	June 2016
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels Fey, Matthias; Lenssen, Jan Eric; Weichert, Frank 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2018.00097	conference	June 2018
KPConv: Flexible and Deformable Convolution for Point Clouds Thomas, Hugues; Qi, Charles R.; Deschaud, Jean-Emmanuel 2019 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2019.00651	conference	October 2019
Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow Ling, Julia; Ruiz, Anthony; Lacaze, Guilhem Journal of Turbomachinery, Vol. 139, Issue 2 https://doi.org/10.1115/1.4034556	journal	October 2016
Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow Ling, Julia; Ruiz, Anthony; Lacaze, Guilhem ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition, Volume 5C: Heat Transfer https://doi.org/10.1115/gt2016-56191	conference	September 2016
Data-driven discovery of partial differential equations Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L. Science Advances, Vol. 3, Issue 4 https://doi.org/10.1126/sciadv.1602614	journal	April 2017
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em SIAM Journal on Scientific Computing, Vol. 40, Issue 1 https://doi.org/10.1137/17M1120762	journal	January 2018
Turbulence Modeling in the Age of Data Duraisamy, Karthik; Iaccarino, Gianluca; Xiao, Heng Annual Review of Fluid Mechanics, Vol. 51, Issue 1 https://doi.org/10.1146/annurev-fluid-010518-040547	journal	January 2019
Machine Learning for Fluid Mechanics Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros Annual Review of Fluid Mechanics, Vol. 52, Issue 1 https://doi.org/10.1146/annurev-fluid-010719-060214	journal	September 2019
Two-Dimensional Turbulence Boffetta, Guido; Ecke, Robert E. Annual Review of Fluid Mechanics, Vol. 44, Issue 1 https://doi.org/10.1146/annurev-fluid-120710-101240	journal	January 2012
Numerical Simulation of Turbulent Flows Rogallo, R. S.; Moin, P. Annual Review of Fluid Mechanics, Vol. 16, Issue 1 https://doi.org/10.1146/annurev.fl.16.010184.000531	journal	January 1984
Scale-Invariance and Turbulence Models for Large-Eddy Simulation Meneveau, Charles; Katz, Joseph Annual Review of Fluid Mechanics, Vol. 32, Issue 1 https://doi.org/10.1146/annurev.fluid.32.1.1	journal	January 2000
Atmospheric Predictability and Two-Dimensional Turbulence Leith, C. E. Journal of the Atmospheric Sciences, Vol. 28, Issue 2 https://doi.org/10.1175/1520-0469(1971)028<0145:APATDT>2.0.CO;2	journal	March 1971
General Circulation Experiments with the Primitive Equations: i. the Basic Experiment* Smagorinsky, J. Monthly Weather Review, Vol. 91, Issue 3 https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2	journal	March 1963
GMLS-Nets: A Framework for Learning from Unstructured Data Trask, Nathaniel; Patel, Ravi; Gross, Ben https://doi.org/10.2172/1763256	report	September 2019
Tests of subgrid-scale models in strained turbulence Mcmillan, O.; Ferziger, J.; Rogallo, R. 13th Fluid and PlasmaDynamics Conference https://doi.org/10.2514/6.1980-1339	conference	February 2013
Improved subgrid-scale models for large-eddy simulation Bardina, J.; Ferziger, J.; Reynolds, W. 13th Fluid and PlasmaDynamics Conference https://doi.org/10.2514/6.1980-1357	conference	February 2013
Application of Supervised Learning to Quantify Uncertainties in Turbulence and Combustion Modeling Tracey, Brendan; Duraisamy, Karthik; Alonso, Juan 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition https://doi.org/10.2514/6.2013-259	conference	January 2013
A Machine Learning Strategy to Assist Turbulence Model Development Tracey, Brendan D.; Duraisamy, Karthikeyan; Alonso, Juan J. 53rd AIAA Aerospace Sciences Meeting https://doi.org/10.2514/6.2015-1287	conference	January 2015
Machine Learning Based Detection of Flow Disturbances Using Surface Pressure Measurements Hou, Wei; Darakananda, Darwin; Eldredge, Jeff AIAA Scitech 2019 Forum https://doi.org/10.2514/6.2019-1148	conference	January 2019
High-order methods for decaying two-dimensional homogeneous isotropic turbulence San, Omer; Staples, Anne E. arXiv https://doi.org/10.48550/arxiv.1212.0920	text	January 2012
Accurate Image Super-Resolution Using Very Deep Convolutional Networks Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu arXiv https://doi.org/10.48550/arxiv.1511.04587	preprint	January 2015
Data-driven discovery of partial differential equations Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L. arXiv https://doi.org/10.48550/arxiv.1609.06401	preprint	January 2016
A neural network approach for the blind deconvolution of turbulent flows Maulik, Romit; San, Omer arXiv https://doi.org/10.48550/arxiv.1706.00912	text	January 2017
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels Fey, Matthias; Lenssen, Jan Eric; Weichert, Frank arXiv https://doi.org/10.48550/arxiv.1711.08920	preprint	January 2017
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework Wu, Jin-Long; Xiao, Heng; Paterson, Eric arXiv https://doi.org/10.48550/arxiv.1801.02762	text	January 2018
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks Vlachas, Pantelis R.; Byeon, Wonmin; Wan, Zhong Y. arXiv https://doi.org/10.48550/arxiv.1802.07486	text	January 2018
Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates Lapeyre, C. J.; Misdariis, A.; Cazard, N. arXiv https://doi.org/10.48550/arxiv.1810.03691	text	January 2018
Predictions of turbulent shear flows using deep neural networks Srinivasan, P. A.; Guastoni, L.; Azizpour, H. arXiv https://doi.org/10.48550/arxiv.1905.03634	preprint	January 2019
Machine Learning for Fluid Mechanics Brunton, Steven; Noack, Bernd; Koumoutsakos, Petros arXiv https://doi.org/10.48550/arxiv.1905.11075	text	January 2019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics Murata, Takaaki; Fukami, Kai; Fukagata, Koji arXiv https://doi.org/10.48550/arxiv.1906.04029	text	January 2019
A deep learning enabler for non-intrusive reduced order modeling of fluid flows Pawar, S.; Rahman, S. M.; Vaddireddy, H. arXiv https://doi.org/10.48550/arxiv.1907.04945	text	January 2019

Similar Records

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

Journal Article · 2018 · Physics of Fluids · OSTI ID:1593571

Maulik, R.; San, O.; Rasheed, A.; +1 more

Subgrid modelling for two-dimensional turbulence using neural networks

Journal Article · 2019 · Journal of Fluid Mechanics · OSTI ID:1593570

Maulik, R.; San, O.; Rasheed, A.; +1 more

Frame invariant neural network closures for Kraichnan turbulence

Journal Article · 2022 · Physica. A · OSTI ID:2421106

Pawar, Suraj; San, Omer; Rasheed, Adil; +1 more

Related Subjects

42 ENGINEERING
Deep learning
Large eddy simulation
Neural networks
Subgrid-scale modeling
Turbulence closure

Title: A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

Citation Formats

References (104)

Similar Records

Related Subjects