3 citations · 5 across the 6 of their papers we have counts for
6 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,…
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…
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'…
Hybrid data driven/thermal simulation model for comfort assessment
Romain Barbedienne, Sara Yasmine Ouerk, Mouadh Yagoubi +3
Machine learning models improve the speed and quality of physical models. However, they require a large amount of data, which is often difficult and costly to acquire. Predicting t…
Rail Crack Propagation Forecasting Using Multi-horizons RNNs
Sara Yasmine Ouerk, Olivier Vo Van, Mouadh Yagoubi
The prediction of rail crack length propagation plays a crucial role in the maintenance and safety assessment of materials and structures. Traditional methods rely on physical mode…
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…