Sitemap

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? How are the learned mappings structured in the reduced subspace? Do these latent representations correlate with key flow parameters?

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 channel flows. Using minimal channel simulations at \(Re_\tau = 200\), we assess the framework’s ability to capture compact, interpretable representations and forecast flow dynamics. Validation includes energy spectra, quadrant analysis, and dynamical tools such as Lyapunov exponents and Poincaré maps.

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 learns compact, interpretable latent representations of flow fields while accurately predicting their evolution using only the cylinder displacement as input. Validated against experimental data across a range of Reynolds numbers, VIVALDy achieves superior reconstruction accuracy and better captures flow statistics than traditional reduced-order models—opening new directions for control and design of VIV-based energy systems.