10 citations · 13 across the 8 of their papers we have counts for
8 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…
A Markov Random Field Multi-Modal Variational AutoEncoder
Fouad Oubari, Mohamed El Baha, Raphael Meunier +2
Recent advancements in multimodal Variational AutoEncoders (VAEs) have highlighted their potential for modeling complex data from multiple modalities. However, many existing approa…
Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2
Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse engineering domains thanks to their capacity to integrate physical laws into deep learning mod…
A Meta-Generation framework for Industrial System Generation
Fouad Oubari, Raphael Meunier, Rodrigue Décatoire +1
Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a chea…
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2
Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINN…
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…
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process
Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +1
Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. Howe…