From the 1 of 10 linked papers with an AI index.
10 papers
Heuristic Learning for Active Flow Control Using Coding Agents
Paul Garnier, Jonathan Viquerat, Elie Hachem
The paper proposes a heuristic learning approach using coding agents to directly discover explicit, interpretable feedback controllers for active flow control, achieving performanc…
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…
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…