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physics.comp-ph2019★ 48 cited
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
Suraj Pawar, Omer San, Adil Rasheed +1
In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models u…
physics.comp-ph2019
A deep learning enabler for non-intrusive reduced order modeling of fluid flows
S. Pawar, S. M. Rahman, H. Vaddireddy +3
In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various de…