3 papers
physics.flu-dyn2026
Divergence-Free Diffusion Models for Incompressible Fluid Flows
Wilfried Genuist, Ãric Savin, Filippo Gatti +1
Generative diffusion models are extensively used in unsupervised and self-supervised machine learning with the aim to generate new samples from a probability distribution estimated…
cs.LG2025
Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction
Wilfried Genuist, Ãric Savin, Filippo Gatti +1
Building on recent advances in scientific machine learning and generative modeling for computational fluid dynamics, we propose a conditional score-based diffusion model designed f…
cs.LG2024
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
Fanny Lehmann, Filippo Gatti, Didier Clouteau
Numerical simulations are essential tools to evaluate the solution of the wave equation in complex settings, such as three-dimensional (3D) domains with heterogeneous properties. H…