77 citations · 95 across the 4 of their papers we have counts for
4 papers
Divergence-Free Diffusion Models for Incompressible Fluid Flows
Wilfried Genuist, Éric 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…
Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction
Wilfried Genuist, Éric 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…
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…
Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
Fanny Lehmann, Filippo Gatti, Michaël Bertin +1
With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditiona…