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