106 citations · 202 across the 13 of their papers we have counts for
13 papers
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…
Aggregation and disaggregation processes in clusters of particles: simple numerical and theoretical insights of the competition in 2D geometries
Louis-Vincent Bouthier, Romain Castellani, Sébastien Manneville +3
Aggregation and disaggregation of clusters of attractive particles under flow are studied from numerical and theoretical points of view. Two-dimensional molecular dynamics simulati…
Proposition of extension of models relating rheological quantities and microscopic structure through the use of a double fractal structure
Louis-Vincent Bouthier, Romain Castellani, Elie Hachem +1
Colloidal suspensions and the relation between their rheology and their microstructure is investigated. The literature showed great evidence of the relation between rheological qua…
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…
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…
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…
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…
Stabilized finite element method for incompressible solid dynamics using an updated Lagrangian formulation
R. Nemer, A. Larcher, T. Coupez +1
This paper proposes a novel way to solve transient linear, and non-linear solid dynamics for compressible, nearly incompressible, and incompressible material in the updated Lagrang…
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…
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…
U-net architectures for fast prediction of incompressible laminar flows
Junfeng Chen, Jonathan Viquerat, Elie Hachem
Machine learning is a popular tool that is being applied to many domains, from computer vision to natural language processing. It is not long ago that its use was extended to physi…
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…
A supervised neural network for drag prediction of arbitrary 2D shapes in low Reynolds number flows
Jonathan Viquerat, Elie Hachem
Despite the significant breakthrough of neural networks in the last few years, their spreading in the field of computational fluid dynamics is very recent, and many applications re…