294 citations · 2.9k across the 83 of their papers we have counts for
38 papers · 1 filter
Accelerated evolution of Burgers turbulence with coarse projective integration and deep learning
Mrigank Dhingra, Omer San, Anne E. Staples
Simulating turbulence to stationarity is a major bottleneck in many engineering problems of practical importance. The problem can be cast as a multiscale problem involving energy r…
Forward Sensitivity Analysis and Mode Dependent Control for Closure Modeling of Galerkin Systems
Shady E. Ahmed, Omer San
Model reduction by projection-based approaches is often associated with losing some of the important features that contribute towards the dynamics of the retained scales. As a resu…
Nonintrusive reduced order modeling of convective Boussinesq flows
Pedram H. Dabaghian, Shady E. Ahmed, Omer San
In this paper, we formulate three nonintrusive methods and systematically explore their performance in terms of the ability to reconstruct the quantities of interest and their pred…
Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation
Suraj Pawar, Omer San
There is a growing interest in developing data-driven reduced-order models for atmospheric and oceanic flows that are trained on data obtained either from high-resolution simulatio…
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…
Frame invariant neural network closures for Kraichnan turbulence
Suraj Pawar, Omer San, Adil Rasheed +1
Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial…