7 papers
Modelisation of chaotic systems with a latent Stochastic Differential Equation
Ismaël Zighed, Ismaël Zighed, Nicolas Thome +2
Stochastic Differential Equations (SDEs) have become a cornerstone of scientific machine learning, though they are predominantly utilized as algorithmic tools for uncertainty quant…
Adjoint-based optimization of the Rayleigh-Bénard instability with melting boundary
Tomas Fullana, Alejandro Quirós RodrÃguez, Vincent Le Chenadec +1
In this work, we propose an adjoint-based optimization procedure to control the onset of the Rayleigh-Bénard instability with a melting front. A novel cut cell method is used to s…
HFNO: an interpretable data-driven decomposition strategy for turbulent flows
Marco Cayuela, Vincent Le Chenadec, Peter Schmid +1
Fourier Neural Operators (FNOs) have demonstrated exceptional accuracy in mapping functional spaces by leveraging Fourier transforms to establish a connection with underlying physi…
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…
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
Priyam Gupta, Peter J. Schmid, Denis Sipp +2
The Koopman operator presents an attractive approach to achieve global linearization of nonlinear systems, making it a valuable method for simplifying the understanding of complex…
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…