180 citations · 944 across the 24 of their papers we have counts for
26 papers
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…
Prospects of federated machine learning in fluid dynamics
Omer San, Suraj Pawar, Adil Rasheed
Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due…
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
Omer San, Suraj Pawar, Adil Rasheed
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained represen…
Decentralized digital twins of complex dynamical systems
Omer San, Suraj Pawar, Adil Rasheed
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and e…
Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation
Suraj Pawar, Omer San
There is a growing interest in developing data-driven reduced-order models for atmospheric and oceanic flows that are trained on data obtained either from high-resolution simulatio…
Physics guided neural networks for modelling of non-linear dynamics
Haakon Robinson, Suraj Pawar, Adil Rasheed +1
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns fro…
Multi-fidelity reinforcement learning framework for shape optimization
Sahil Bhola, Suraj Pawar, Prasanna Balaprakash +1
Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized…
Frame invariant neural network closures for Kraichnan turbulence
Suraj Pawar, Omer San, Adil Rasheed +1
Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial…
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…
Multi-fidelity information fusion with concatenated neural networks
Suraj Pawar, Omer San, Prakash Vedula +2
Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many appl…
On closures for reduced order models A spectrum of first-principle to machine-learned avenues
Shady E. Ahmed, Suraj Pawar, Omer San +3
For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr-Sommer…
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…
Physics guided machine learning using simplified theories
Suraj Pawar, Omer San, Burak Aksoylu +2
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences…
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…
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
Shady Ahmed, Suraj Pawar, Omer San +1
In this paper, 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. We bui…
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…