Talks & Presentations
Data-driven modeling of near-wall turbulence using β-variational autoencoder, transformers, and adversarial loss
This talk presented an extension of the work shown at EUROMECH 629, with a deeper analysis of latent space forecasting for near-wall turbulence. The framework combines a β-variational autoencoder (β-VAE-GAN) for unsupervised feature extraction and a decoder-only transf...
Vivaldy: AI-Driven Low-Order Modeling of Vortex-Induced-Vibrations
How can we efficiently model turbulent flows in vortex-induced vibration (VIV) systems for energy harvesting? In this talk, I introduced VIVALDy, a deep generative framework combining a β-VAE-GAN with masked convolutions and a bidirectional transformer. The model learn...
Data-driven modeling of near-wall turbulence using β-variational autoencoder, transformers, and adversarial loss
Can machine learning help us understand and predict near-wall turbulence? In this talk, I presented a framework combining a β-variational autoencoder (β-VAE) for unsupervised feature extraction and a unidirectional transformer for temporal prediction of turbulent chann...
Reduced-oreder modeling of experimental turbulent flows: from linear projection-based methods to autoencoders
Can deep learning effectively compress and reconstruct turbulent flows? I will present our analysis, exploring autoencoders (AEs) and variational autoencoders (VAEs). We address key questions: How well do AEs and VAEs reconstruct the flow at extreme compression rates? ...