294 citations · 2.9k across the 83 of their papers we have counts for
5 papers · 2 filters
An evolve-then-correct reduced order model for hidden fluid dynamics
Suraj Pawar, Shady E. Ahmed, O. San +1
In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. S…
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors
Harsha Vaddireddy, Adil Rasheed, Anne E Staples +1
In this study we put forth a modular approach for distilling hidden flow physics in discrete and sparse observations. To address functional expressiblity, a key limitation of the b…
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
Suraj Pawar, Shady E. Ahmed, Omer San +1
In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant…
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…