activity
20192022
most citedOn closures for reduced order models A spectrum of first-principle to machine-learned avenues

147 citations · 272 across the 9 of their papers we have counts for

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Showing physics.comp-phShow all

7 papers · 1 filter

physics.comp-ph2021

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.comp-ph2020

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…

physics.comp-ph2020

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…

physics.comp-ph2020

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,…

physics.comp-ph202062 cited

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…

physics.comp-ph2019

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…