Data-driven modeling of near-wall turbulence using β-variational autoencoder, transformers, and adversarial loss
Date:
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.
