no code implementations • 15 Mar 2023 • Shady E. Ahmed, Panos Stinis
Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables.
no code implementations • 25 May 2022 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu, Alessandro Veneziani
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 of reduced order models (ROMs) at a modest computational cost.
1 code implementation • 15 Oct 2021 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu
Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space.
1 code implementation • 17 Jun 2020 • Shady E. Ahmed, Omer San, Kursat Kara, Rami Younis, Adil Rasheed
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws.
1 code implementation • 21 May 2020 • Shady E. Ahmed, Kinjal Bhar, Omer San, Adil Rasheed
In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models.
Dynamical Systems Fluid Dynamics
1 code implementation • 14 Dec 2019 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu
In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes.
Fluid Dynamics Dynamical Systems Computational Physics