output
20172026
most citedSub-grid modelling for two-dimensional turbulence using neural networks

294 citations

Showing physics.flu-dynShow all

5 papers · 1 filter

physics.flu-dyn2022★ 2 cited

Generalization and stabilization of exact scattering solutions for spherical symmetric scatterers

Jon Vegard Venås, Trond Jenserud

In a previous work the authors described a fast high-fidelity computer model for acoustic scattering from multi-layered elastic spheres. This work is now extended with a scaling st…

physics.flu-dyn2021★ 49 cited

Nonlinear proper orthogonal decomposition for convection-dominated flows

Shady E. Ahmed, Omer San, Adil Rasheed +1

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven mo…

physics.flu-dyn2020★ 29 cited

Multifidelity Computing for Coupling Full and Reduced Order Models

Shady E. Ahmed, Omer San, Kursat Kara +2

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering a…

physics.flu-dyn2018★ 3 cited

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

Romit Maulik, Omer San, Adil Rasheed +1

In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for s…

physics.flu-dyn2018★ 294 cited

Sub-grid modelling for two-dimensional turbulence using neural networks

Romit Maulik, Omer San, Adil Rasheed +1

In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method…