Surrogate modelling can alleviate the computational burden of design activities as they rely
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …

This paper investigates the use of data-driven methods for the reconstruction of unsteady
fluid flow fields. The proposed framework is based on the combination of machine learning …

We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields
named FluidDiff. By performing a diffusion process, the model is able to learn a complex …

Perception of the full state is an essential technology to support the monitoring, analysis, and
design of physical systems, one of whose challenges is to recover global field from sparse …

This paper investigates the unsteady flow around a high-rise building using OpenFoam. A
Vortex Method is developed to generate upstream unsteady fluctuations that are validated …

We propose a nonlinear manifold learning technique based on deep convolutional
autoencoders that is appropriate for model order reduction of physical systems in complex …

Generative models offer the possibility to accelerate and potentially substitute parts of the
often expensive traditional design optimization process. However, current implementations …

La mécanique des fluides est présente dans de nombreuses thématiques industrielles telles
que la santé, le transport et l'énergie. Pour modéliser, contrôler et réduire les écoulements …

This paper gives a framework for the data-driven estimation of an unsteady fluid flow field.
The strategy combines machine learning tools for the reduction, the reconstruction and the …