108 citations · 317 across the 9 of their papers we have counts for
9 papers
Proposal for Numerical Benchmarking of Fluid-Structure Interaction in Cerebral Aneurysms
Aurèle Goetz, Pablo Jeken Rico, Yves Chau +3
Computational fluid dynamics is intensively used to deepen the understanding of aneurysm growth and rupture in the attempt to support physicians during therapy planning. Numerous s…
Adaptive Immersed Mesh Method (AIMM) for Fluid Structure Interaction
R. Nemer, A. Larcher, E. Hachem
Our paper proposes an innovative approach for modeling Fluid-Structure Interaction (FSI). Our method combines both traditional monolithic and partitioned approaches, creating a hyb…
A review on deep reinforcement learning for fluid mechanics: an update
Jonathan Viquerat, Philippe Meliga, Elie Hachem
In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning (DRL) techniques has increased at fast pace, leading to a growing bibliog…
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…
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…
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows
H. Ghraieb, J. Viquerat, A. Larcher +2
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerat…
A review on Deep Reinforcement Learning for Fluid Mechanics
Paul Garnier, Jonathan Viquerat, Jean Rabault +3
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previousl…
Analysis of a fractional-step scheme for the P1 radiative diffusion model
Raphaele Herbin, Thierry Gallouët, Jean-Claude Latché +1
We address in this paper a nonlinear parabolic system, which is built to retain the main mathematical difficulties of the P1 radiative diffusion physical model. We propose a finite…