147 citations · 272 across the 9 of their papers we have counts for
6 papers · 1 filter
Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
Shady E. Ahmed, Omer San, Adil Rasheed +2
We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy…
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…
A long short-term memory embedding for hybrid uplifted reduced order models
Shady E. Ahmed, Omer San, Adil Rasheed +1
In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embed…
Sampling and resolution characteristics in reduced order models of shallow water equations: intrusive vs non-intrusive
Shady E. Ahmed, Omer San, Diana A. Bistrian +1
We investigate the sensitivity of reduced order models (ROMs) to training data resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD)…
Memory embedded non-intrusive reduced order modeling of non-ergodic flows
Shady E. Ahmed, Sk. Mashfiqur Rahman, Omer San +2
Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniq…
A dynamic closure modeling framework for model order reduction of geophysical flows
Sk. Mashfiqur Rahman, Shady E. Ahmed, Omer San
In this paper, a dynamic closure modeling approach has been derived to stabilize the projection-based reduced order models in the long-term evolution of forced-dissipative dynamica…