8 citations · 9 across the 9 of their papers we have counts for
10 papers · 1 filter
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…
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…