15 citations · 20 across the 13 of their papers we have counts for
15 papers
Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer +2
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model…
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
Mouadh Yagoubi, David Danan, Milad Leyli-Abadi +15
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computa…
NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models
Mouadh Yagoubi, Yasser Dahou, Billel Mokeddem +12
Existing benchmarks have proven effective for assessing the performance of fully trained large language models. However, we find striking differences in the early training stages o…
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning
Chetra Mang, Axel TahmasebiMoradi, Mouadh Yagoubi
We propose a new methodology for parametric domain decomposition using iterative principal component analysis. Starting with iterative principle component analysis, the high dimens…
An adaptive sampling algorithm for data-generation to build a data-manifold for physical problem surrogate modeling
Chetra Mang, Axel TahmasebiMoradi, David Danan +1
Physical models classically involved Partial Differential equations (PDE) and depending of their underlying complexity and the level of accuracy required, and known to be computati…
Constrained Recurrent Bayesian Forecasting for Crack Propagation
Sara Yasmine Ouerk, Olivier Vo Van, Mouadh Yagoubi
Predictive maintenance of railway infrastructure, especially railroads, is essential to ensure safety. However, accurate prediction of crack evolution represents a major challenge…
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…
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…
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…
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…