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20192026
most citedA review on deep reinforcement learning for fluid mechanics: an update

108 citations · 282 across the 19 of their papers we have counts for

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physics.flu-dyn2026

ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows

Théodore Michel, Antoine Campos, Alban Dujardin +3

Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, their development is limited by t…

physics.flu-dyn2026

Dripping-onto-droplet capillary breakup

Ricardo El Khoury, Kindness Isukwem, Elie Hachem +1

This experimental, numerical, and theoretical study investigates the capillary thinning and breakup of Newtonian filaments formed following the coalescence of a millimetric-nozzle-…

physics.flu-dyn2024

Retraction of levitating drops

Kindness Isukwem, Elie Hachem, Anselmo Pereira

This theoretical and numerical study focuses on the physical mechanisms driving the retraction of levitating Newtonian micrometric/millimetric/centimetric drops surrounded by air a…

physics.flu-dyn2023

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…

physics.flu-dyn2021★ 108 cited

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…