Karthik Duraisamy
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Turbulence modeling in the age of data
K Duraisamy, G Iaccarino, H Xiao
Annual Review of Fluid Mechanics, 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
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
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
Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils
AP Singh, S Medida, K Duraisamy
AIAA Journal, 1-13, 2017
A machine learning strategy to assist turbulence model development
BD Tracey, K Duraisamy, JJ Alonso
53rd AIAA Aerospace Sciences Meeting, 1287, 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
New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques
K Duraisamy, ZJ Zhang, AP Singh
53rd AIAA Aerospace Sciences Meeting, 2015
Machine Learning Methods for Data-Driven Turbulence Modeling
ZJ Zhang, K Duraisamy
AIAA Aviation 2015, 2015
Using field inversion to quantify functional errors in turbulence closures
AP Singh, K Duraisamy
Physics of Fluids 28 (4), 2016
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
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
K Duraisamy
Physical Review Fluids 6 (5), 050504, 2021
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
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
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
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
Mechanics of viscous vortex reconnection
F Hussain, K Duraisamy
Physics of Fluids 23 (2), 2011
Data-driven discovery of closure models
S Pan, K Duraisamy
SIAM Journal on Applied Dynamical Systems 17 (4), 2381-2413, 2018
Long-time predictive modeling of nonlinear dynamical systems using neural networks
S Pan, K Duraisamy
Complexity 2018, 2018
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
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