Article-Journal

LDDMM stochastic interpolants: An application to domain uncertainty quantification in hemodynamics

Shape-informed graph neural networks and data assimilation: application to velocity and pressure reconstruction in aortic blood flow

Efficient numerical strategies for entropy-regularized semi-discrete optimal transport

Friedrichs' systems discretized with the DGM: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity solutions

Explicable hyper-reduced order models with autoencoders

Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions

Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method

A Local Approach to Parameter Space Reduction for Regression and Classification Tasks

Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering

Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method