15 citations · 22 across the 11 of their papers we have counts for
11 papers
NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
Mouadh Yagoubi, David Danan, Milad Leyli-abadi +8
The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However,…
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau +2
This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational dat…
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
Mouadh Yagoubi, Milad Leyli-Abadi, David Danan +6
The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physica…
Multi-Level GNN Preconditioner for Solving Large Scale Problems
Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney +3
Large-scale numerical simulations often come at the expense of daunting computations. High-Performance Computing has enhanced the process, but adapting legacy codes to leverage par…
Interpretable learning of effective dynamics for multiscale systems
Emmanuel Menier, Sebastian Kaltenbach, Mouadh Yagoubi +2
The modeling and simulation of high-dimensional multiscale systems is a critical challenge across all areas of science and engineering. It is broadly believed that even with today'…
Meta-Learning for Airflow Simulations with Graph Neural Networks
Wenzhuo Liu, Mouadh Yagoubi, Marc Schoenauer
The field of numerical simulation is of significant importance for the design and management of real-world systems, with partial differential equations (PDEs) being a commonly used…
An Implicit GNN Solver for Poisson-like problems
Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney +3
This paper presents -GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions. By leveraging the Implicit…
Continuous Methods : Adaptively intrusive reduced order model closure
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +4
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) o…
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat +3
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to…
Continuous Methods : Hamiltonian Domain Translation
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +2
This paper proposes a novel approach to domain translation. Leveraging established parallels between generative models and dynamical systems, we propose a reformulation of the Cycl…
CD-ROM: Complemented Deep-Reduced Order Model
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +2
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems. However, the applicability of th…