R
Romit Maulik
Researcher at Argonne National Laboratory
Publications - 103
Citations - 2303
Romit Maulik is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 19, co-authored 81 publications receiving 1411 citations. Previous affiliations of Romit Maulik include Oklahoma State University–Stillwater & Oak Ridge National Laboratory.
Papers
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Subgrid modelling for two-dimensional turbulence using neural networks
TL;DR: The proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels, which represents a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.
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A neural network approach for the blind deconvolution of turbulent flows
Romit Maulik,Omer San +1 more
TL;DR: In this article, a blind deconvolution network is proposed for large eddy simulations, where the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel.
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Neural network closures for nonlinear model order reduction
Omer San,Romit Maulik +1 more
TL;DR: In this paper, a robust machine learning framework for projection-based reduced-order modeling of such nonlinear and nonstationary systems is presented. But the model is not robust with respect to parameter changes nor cost-effective enough for handling the nonlinear dependence of complex dynamical systems.
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Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
TL;DR: This study demonstrates that a truncated system of only two latent-space dimensions can reproduce a sharp advecting shock profile for the viscous Burgers equation with very low viscosities, and a twelve-dimensional latent space can recreate the evolution of the inviscid shallow water equations.
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An artificial neural network framework for reduced order modeling of transient flows
TL;DR: In this article, a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows is proposed to provide accurate predictions of non-stationary state variables when the control parameter values vary.