180 citations · 944 across the 24 of their papers we have counts for
14 papers · 1 filter
Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh Benard convection
Saeed Akbari, Suraj Pawar, Omer San
Recent developments in diagnostic and computing technologies offer to leverage numerous forms of nonintrusive modeling approaches from data where machine learning can be used to bu…
Hyperparameter Search using Genetic Algorithm for Surrogate Modeling of Geophysical Flows
Suraj Pawar, Omer San, Gary G. Yen
The computational models for geophysical flows are computationally very expensive to employ in multi-query tasks such as data assimilation, uncertainty quantification, and hence su…
Model fusion with physics-guided machine learning
Suraj Pawar, Omer San, Aditya Nair +2
The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatio-temporal scales have enabled the ra…
A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations
Suraj Pawar, Omer San, Adil Rasheed +1
This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitab…
Hybrid analysis and modeling for next generation of digital twins
Suraj Pawar, Shady E. Ahmed, Omer San +1
The physics-based modeling has been the workhorse for many decades in many scientific and engineering applications ranging from wind power, weather forecasting, and aircraft design…
Distributed deep reinforcement learning for simulation control
Suraj Pawar, Romit Maulik
Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contai…
Interface learning in fluid dynamics: statistical inference of closures within micro-macro coupling models
Suraj Pawar, Shady E. Ahmed, Omer San
Many complex multiphysics systems in fluid dynamics involve using solvers with varied levels of approximations in different regions of the computational domain to resolve multiple…
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
Shady Ahmed, Suraj Pawar, Omer San +2
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improv…
Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows
Suraj Pawar, Omer San
In the past couple of years, there is a proliferation in the use of machine learning approaches to represent subgrid scale processes in geophysical flows with an aim to improve the…
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
Suraj Pawar, Shady E. Ahmed, Omer San +2
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast cov…
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…
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…