Romit Maulik
Romit Maulik
Argonne National Laboratory; Incoming Assistant Professor Pennsylvania State University
Verified email at - Homepage
Cited by
Cited by
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
A neural network approach for the blind deconvolution of turbulent flows
R Maulik, O San
Journal of Fluid Mechanics 831, 151-181, 2017
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), 037106, 2021
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
Neural network closures for nonlinear model order reduction
O San, R Maulik
Advances in Computational Mathematics 44, 1717-1750, 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
Extreme learning machine for reduced order modeling of turbulent geophysical flows
O San, R Maulik
Physical Review E 97 (4), 042322, 2018
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
R Maulik, O San, A Rasheed, P Vedula
Physics of Fluids 30 (12), 125109, 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
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
Machine learning closures for model order reduction of thermal fluids
O San, R Maulik
Applied Mathematical Modelling 60, 681-710, 2018
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
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), 047110, 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
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
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
Non-autoregressive time-series methods for stable parametric reduced-order models
R Maulik, B Lusch, P Balaprakash
Physics of Fluids 32 (8), 087115, 2020
Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels
B Hamzi, R Maulik, H Owhadi
Proceedings of the Royal Society A 477 (2252), 20210326, 2021
A stable and scale-aware dynamic modeling framework for subgrid-scale parameterizations of two-dimensional turbulence
R Maulik, O San
Computers & Fluids 158, 11-38, 2017
Resolution and energy dissipation characteristics of implicit LES and explicit filtering models for compressible turbulence
R Maulik, O San
Fluids 2 (2), 14, 2017
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