Subgrid modelling for two-dimensional turbulence using neural networks R Maulik, O San, A Rasheed, P Vedula Journal of Fluid Mechanics 858, 122-144, 2019 | 324 | 2019 |
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders R Maulik, B Lusch, P Balaprakash Physics of Fluids 33 (3), 2021 | 248 | 2021 |
A neural network approach for the blind deconvolution of turbulent flows R Maulik, O San Journal of Fluid Mechanics 831, 151-181, 2017 | 218 | 2017 |
An artificial neural network framework for reduced order modeling of transient flows O San, R Maulik, M Ahmed Communications in Nonlinear Science and Numerical Simulation 77, 271-287, 2019 | 164 | 2019 |
Neural network closures for nonlinear model order reduction O San, R Maulik Advances in Computational Mathematics 44 (6), 1717-1750, 2018 | 153 | 2018 |
Time-series learning of latent-space dynamics for reduced-order model closure R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu Physica D: Nonlinear Phenomena 405, 132368, 2020 | 125 | 2020 |
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning K Fukami, R Maulik, N Ramachandra, K Fukagata, K Taira Nature Machine Intelligence 3 (11), 945-951, 2021 | 122 | 2021 |
Probabilistic neural networks for fluid flow surrogate modeling and data recovery R Maulik, K Fukami, N Ramachandra, K Fukagata, K Taira Physical Review Fluids 5 (10), 104401, 2020 | 110* | 2020 |
A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations R Maulik, H Sharma, S Patel, B Lusch, E Jennings Computers & Fluids 227, 104777, 2021 | 106* | 2021 |
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence R Maulik, O San, A Rasheed, P Vedula Physics of Fluids 30 (12), 2018 | 103 | 2018 |
Extreme learning machine for reduced order modeling of turbulent geophysical flows O San, R Maulik Physical Review E 97 (4), 042322, 2018 | 100 | 2018 |
Sub-grid scale model classification and blending through deep learning R Maulik, O San, JD Jacob, C Crick Journal of Fluid Mechanics 870, 784-812, 2019 | 89 | 2019 |
Machine learning closures for model order reduction of thermal fluids O San, R Maulik Applied Mathematical Modelling 60, 681-710, 2018 | 80 | 2018 |
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil SA Renganathan, R Maulik, V Rao Physics of Fluids 32 (4), 2020 | 76 | 2020 |
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization SA Renganathan, R Maulik, J Ahuja Aerospace Science and Technology 111, 106522, 2021 | 74 | 2021 |
Recurrent neural network architecture search for geophysical emulation R Maulik, R Egele, B Lusch, P Balaprakash SC20: International Conference for High Performance Computing, Networking …, 2020 | 53 | 2020 |
Autodeuq: Automated deep ensemble with uncertainty quantification R Egele, R Maulik, K Raghavan, B Lusch, I Guyon, P Balaprakash 2022 26th International Conference on Pattern Recognition (ICPR), 1908-1914, 2022 | 46 | 2022 |
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems AJ Linot, JW Burby, Q Tang, P Balaprakash, MD Graham, R Maulik Journal of Computational Physics 474, 111838, 2023 | 41 | 2023 |
Data-driven wind turbine wake modeling via probabilistic machine learning S Ashwin Renganathan, R Maulik, S Letizia, GV Iungo Neural Computing and Applications, 1-16, 2022 | 34 | 2022 |
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation R Maulik, T Botsas, N Ramachandra, LR Mason, I Pan Physica D: Nonlinear Phenomena 416, 132797, 2021 | 34 | 2021 |