| Turbulence modeling in the age of data K Duraisamy, G Iaccarino, H Xiao Annual Review of Fluid Mechanics, 2019 | 1400 | 2019 |
| A paradigm for data-driven predictive modeling using field inversion and machine learning EJ Parish, K Duraisamy Journal of Computational Physics 305, 758-774, 2016 | 646 | 2016 |
| Prediction of aerodynamic flow fields using convolutional neural networks S Bhatnagar, Y Afshar, S Pan, K Duraisamy, S Kaushik Computational Mechanics 64, 525-545, 2019 | 562 | 2019 |
| Modal analysis of fluid flows: Applications and outlook K Taira, MS Hemati, SL Brunton, Y Sun, K Duraisamy, S Bagheri, ... AIAA Journal 58 (3), 998-1022, 2020 | 527 | 2020 |
| Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils AP Singh, S Medida, K Duraisamy AIAA Journal, 1-13, 2017 | 471 | 2017 |
| A machine learning strategy to assist turbulence model development BD Tracey, K Duraisamy, JJ Alonso 53rd AIAA Aerospace Sciences Meeting, 1287, 2015 | 390 | 2015 |
| Liszt: a domain specific language for building portable mesh-based PDE solvers Z DeVito, N Joubert, F Palacios, S Oakley, M Medina, M Barrientos, ... Proceedings of 2011 International Conference for High Performance Computing …, 2011 | 333 | 2011 |
| New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques K Duraisamy, ZJ Zhang, AP Singh 53rd AIAA Aerospace Sciences Meeting, 2015 | 277 | 2015 |
| Machine Learning Methods for Data-Driven Turbulence Modeling ZJ Zhang, K Duraisamy AIAA Aviation 2015, 2015 | 236 | 2015 |
| Using field inversion to quantify functional errors in turbulence closures AP Singh, K Duraisamy Physics of Fluids 28 (4), 2016 | 228 | 2016 |
| Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence K Duraisamy Physical Review Fluids 6 (5), 050504, 2021 | 213 | 2021 |
| Application of supervised learning to quantify uncertainties in turbulence and combustion modeling B Tracey, K Duraisamy, J Alonso 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and …, 2013 | 175 | 2013 |
| Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics J Xu, K Duraisamy Computer Methods in Applied Mechanics and Engineering 372, 113379, 2020 | 172 | 2020 |
| Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability S Pan, K Duraisamy SIAM Journal on Applied Dynamical Systems 19 (1), 480-509, 2020 | 155 | 2020 |
| Computational analysis of shrouded wind turbine configurations using a 3-dimensional RANS solver AC Aranake, VK Lakshminarayan, K Duraisamy Renewable Energy 75, 818-832, 2015 | 151* | 2015 |
| Flow physics and RANS modelling of oblique shock/turbulent boundary layer interaction B Morgan, K Duraisamy, N Nguyen, S Kawai, SK Lele Journal of Fluid Mechanics 729, 231-284, 2013 | 118 | 2013 |
| Data-driven discovery of closure models S Pan, K Duraisamy SIAM Journal on Applied Dynamical Systems 17 (4), 2381-2413, 2018 | 112 | 2018 |
| Long-time predictive modeling of nonlinear dynamical systems using neural networks S Pan, K Duraisamy Complexity 2018, 2018 | 112 | 2018 |
| Mechanics of viscous vortex reconnection F Hussain, K Duraisamy Physics of Fluids 23 (2), 2011 | 109 | 2011 |
| Large-eddy simulations of a normal shock train in a constant-area isolator B Morgan, K Duraisamy, SK Lele AIAA journal 52 (3), 539-558, 2014 | 106 | 2014 |
Eric J. ParishSandia National LabsVerified email at sandia.gov
