108 citations · 229 across the 14 of their papers we have counts for
14 papers
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…
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…
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…