108 citations · 282 across the 19 of their papers we have counts for
20 papers
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…
Heuristic Learning for Active Flow Control Using Coding Agents
Paul Garnier, Jonathan Viquerat, Elie Hachem
Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging. Deep reinforcem…
Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
Amir Bazzi, David Cardinaux, Ramy Nemer +3
Learning-based surrogates for partial differential equations have recently matched the accuracy of classical solvers while achieving orders-of-magnitude speedups, predominantly in…
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-…
Mesh Based Simulations with Spatial and Temporal awareness
Paul Garnier, Vincent Lannelongue, Elie Hachem
Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and Transformers, have become a new important approach for acceleratin…
Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment
Paul Garnier, Pablo Jeken-Rico, Vincent Lannelongue +11
Intracranial aneurysms remain a major cause of neurological morbidity and mortality worldwide, where rupture risk is tightly coupled to local hemodynamics particularly wall shear s…
Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
Eliane Younes, Elie Hachem, Marc Bernacki
Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning ap…
Curriculum Learning for Mesh-based simulations
Paul Garnier, Vincent Lannelongue, Elie Hachem
Graph neural networks (GNNs) have emerged as powerful surrogates for mesh-based computational fluid dynamics (CFD), but training them on high-resolution unstructured meshes with hu…
TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis
Clément Hervé, Paul Garnier, Jonathan Viquerat +1
Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate and model due to limited annotated 3D data. We propose a cross-domain feature-transfe…
Automated discovery of finite volume schemes using Graph Neural Networks
Paul Garnier, Jonathan Viquerat, Elie Hachem
Graph Neural Networks (GNNs) have deeply modified the landscape of numerical simulations by demonstrating strong capabilities in approximating solutions of physical systems. Howeve…
Training Transformers for Mesh-Based Simulations
Paul Garnier, Vincent Lannelongue, Jonathan Viquerat +1
Simulating physics using Graph Neural Networks (GNNs) is predominantly driven by message-passing architectures, which face challenges in scaling and efficiency, particularly in han…
Dragonfly: a modular deep reinforcement learning library
Jonathan Viquerat, Paul Garnier, Amirhossein Bateni +1
Dragonfly is a deep reinforcement learning library focused on modularity, in order to ease experimentation and developments. It relies on a json serialization that allows to swap b…
MeshMask: Physics-Based Simulations with Masked Graph Neural Networks
Paul Garnier, Vincent Lannelongue, Jonathan Viquerat +1
We introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40\% of input…
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…
Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics
Paul Garnier, Jonathan Viquerat, Elie Hachem
Advancement in finite element methods have become essential in various disciplines, and in particular for Computational Fluid Dynamics (CFD), driving research efforts for improved…
Beacon, a lightweight deep reinforcement learning benchmark library for flow control
Jonathan Viquerat, Philippe Meliga, Pablo Jeken +1
Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing…
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…
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…
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…