180 citations · 944 across the 24 of their papers we have counts for
7 papers · 1 filter
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…