From the 1 of 6 linked papers with an AI index.
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Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers
Rémy Vallot, Florian de Vuyst, Thibault Dairay +1
The paper introduces a two‑stage method that learns features from precomputed Newton trajectories to predict a surrogate solution and then apply a cheap corrective step, providing…
Correcting Source Mismatch in Flow Matching with Radial-Angular Transport
Fouad Oubari, Mathilde Mougeot
Flow Matching is typically built from Gaussian sources and Euclidean probability paths. For heavy-tailed or anisotropic data, however, a Gaussian source induces a structural mismat…
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…
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…
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…