7 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…