1 citations · 1 across the 4 of their papers we have counts for
4 papers
Modelisation of chaotic systems with a latent Stochastic Differential Equation
Ismaël Zighed, Nicolas Thome, Patrick Gallinari +1
Stochastic Differential Equations (SDEs) have become a cornerstone of scientific machine learning, though they are predominantly utilized as algorithmic tools for uncertainty quant…
Leveraging Scale Separation and Stochastic Closure for Data-Driven Prediction of Chaotic Dynamics
Ismaël Zighed, Nicolas Thome, Patrick Gallinari +1
Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions acr…
UP-dROM : Uncertainty-Aware and Parametrised dynamic Reduced-Order Model, application to unsteady flows
Ismaël Zighed, Nicolas Thome, Patrick Gallinari +1
Reduced order models (ROMs) play a critical role in fluid mechanics by providing low-cost predictions, making them an attractive tool for engineering applications. However, for ROM…
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Nathan Painchaud, Jérémie Stym-Popper, Pierre-Yves Courand +4
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provi…