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20192022
most citedGraph neural networks for laminar flow prediction around random 2D shapes

106 citations · 202 across the 13 of their papers we have counts for

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physics.flu-dyn2022★ 1 cited

Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows

Aakash Patil, Jonathan Viquerat, Elie Hachem

A convolutional encoder-decoder-based transformer model is proposed for autoregressively training on spatio-temporal data of turbulent flows. The prediction of future fluid flow fi…

physics.flu-dyn2021★ 106 cited

Graph neural networks for laminar flow prediction around random 2D shapes

Junfeng Chen, Elie Hachem, Jonathan Viquerat

In the recent years, the domain of fast flow field prediction has been vastly dominated by pixel-based convolutional neural networks. Yet, the recent advent of graph convolutional…

physics.flu-dyn2021★ 14 cited

Robust deep learning for emulating turbulent viscosities

Aakash Patil, Jonathan Viquerat, George El Haber +1

From the simplest models to complex deep neural networks, modeling turbulence with machine learning techniques still offers multiple challenges. In this context, the present contri…

physics.flu-dyn2021

A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles

Junfeng Chen, Jonathan Viquerat, Frederic Heymes +1

Over the past few years, deep learning methods have proved to be of great interest for the computational fluid dynamics community, especially when used as surrogate models, either…

physics.flu-dyn2021★ 1 cited

Folding instabilities in non-Newtonian viscous sheets: shear thinning and shear thickening effects

Anselmo Pereira, Nicolas Valade, Elie Hachem +1

In this work, we extend the analyses devoted to Newtonian viscous fluids previously reported by Ribe [Physical Review E 68, 036305 (2003)], by investigating shear thickening (dilat…

physics.flu-dyn2020★ 4 cited

Deep reinforcement learning for the control of conjugate heat transfer with application to workpiece cooling

Elie Hachem, Hassan Ghraieb, Jonathan Viquerat +2

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the control of conjugate heat transfer systems governed by the coupled Navier--Stokes and…

physics.flu-dyn2020

Analysis and comparisons of various models in cold spray simulations : towards high fidelity simulations

Louis-Vincent Bouthier, Elie Hachem

Cold spray technology is a quickly growing manufacturing technology which impacts lots of industries. Despite many years of studies about the comprehension of the phenomena and the…

physics.flu-dyn2019★ 4 cited

Exploiting locality and physical invariants to design effective Deep Reinforcement Learning control of the unstable falling liquid film

Vincent Belus, Jean Rabault, Jonathan Viquerat +3

Instabilities arise in a number of flow configurations. One such manifestation is the development of interfacial waves in multiphase flows, such as those observed in the falling li…