48 citations · 48 across the 2 of their papers we have counts for
6 papers
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…
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…
A novel quantum grid search algorithm and its application
Alok Shukla, Prakash Vedula
In this paper we present a novel quantum algorithm, namely the quantum grid search algorithm, to solve a special search problem. Suppose non-empty buckets are given, such tha…
Invariant compact finite difference schemes
Ersin Ozbenli, Prakash Vedula
In this paper, we propose a method, that is based on equivariant moving frames, for development of high order accurate invariant compact finite difference schemes that preserve Lie…
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
Romit Maulik, Omer San, Adil Rasheed +1
In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for s…
Generalized deconvolution procedure for structural modeling of turbulence
Omer San, Prakash Vedula
Approximate deconvolution forms a mathematical framework for the structural modeling of turbulence. The sub-filter scale flow quantities are typically recovered by using the Van Ci…