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…
Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation
Ismaël Zighed, Andrea Nóvoa, Luca Magri +1
We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while requiring only a fraction of the…
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…