186 citations
- Centre BorelliFR8 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- École Nationale Supérieure d’Informatique pour l’Industrie et l’EntrepriseFR5 papers
- École Normale Supérieure Paris-SaclayFR3 papers
- Altair Engineering (France)FR2 papers
- Biomécanique et BioingénierieFR2 papers
- École Normale Supérieure - PSLFR2 papers
- ESPCI ParisFR2 papers
- Laboratoire de Biologie et Pharmacologie AppliquéeFR2 papers
- Laboratoire de Mécanique, Modélisation & Procédés PropresFR2 papers
- Laboratoire Interdisciplinaire de PhysiqueFR2 papers
- Lyon 1 UniversitéFR2 papers
16 papers
Deep Generative Methods and Tire Architecture Design
Fouad Oubari, Raphael Meunier, Rodrigue Décatoire +1
As deep generative models proliferate across the AI landscape, industrial practitioners still face critical yet unanswered questions about which deep generative models best suit co…
Multi-Component VAE with Gaussian Markov Random Field
Fouad Oubari, Mohamed El-Baha, Raphael Meunier +2
Multi-component datasets with intricate dependencies, like industrial assemblies or multi-modal imaging, challenge current generative modeling techniques. Existing Multi-component…
Giant mobility of surface-trapped ionic charges following liquid tribocharging
Zouhir Benrahla, Tristan Saide, Louis Burnaz +3
The sliding motion of aqueous droplets on hydrohobic surfaces leads to charge separation at the trailing edge, with implications from triple-line friction to hydrovoltaic energy ge…
Machine-Learning Enhanced Predictors for Accelerated Convergence of Partitioned Fluid-Structure Interaction Simulations
Azzeddine Tiba, Thibault Dairay, Florian de Vuyst +2
Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple couplin…
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot +1
This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a practical solution to the lac…
Non-intrusive reduced order models for partitioned fluid-structure interactions
Azzeddine Tiba, Thibault Dairay, Florian de Vuyst +2
The main goal of this work is to develop a data-driven Reduced Order Model (ROM) strategy from high-fidelity simulation result data of a Full Order Model (FOM). The goal is to pred…