62 citations · 135 across the 4 of their papers we have counts for
9 papers
Sketching Methods for Dynamic Mode Decomposition in Spherical Shallow Water Equations
Shady E. Ahmed, Omer San, Diana A. Bistrian +1
Dynamic mode decomposition (DMD) is an emerging methodology that has recently attracted computational scientists working on nonintrusive reduced order modeling. One of the major st…
A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods
R. Fu, D. Xiao, I. M. Navon +1
This paper presents a new data-driven non-intrusive reduced-order model(NIROM) that outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder model. This…
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…
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…
An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
M. Cheng, F. Fang, C. C. Pain +1
Considering the high computation cost produced in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in r…
Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
M. Cheng, F. Fang, C. C. Pain +1
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly p…