147 citations · 639 across the 19 of their papers we have counts for
11 papers · 1 filter
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, eclecticism, and multifidelity computing toward digital twin revolution
Omer San, Adil Rasheed, Trond Kvamsdal
Most modeling approaches lie in either of the two categories: physics-based or data-driven. Recently, a third approach which is a combination of these deterministic and statistical…
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…
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…
Interface learning of multiphysics and multiscale systems
Shady E. Ahmed, Omer San, Kursat Kara +2
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges,…
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…