Data-driven modeling of near-wall turbulence using β-variational autoencoder, transformers, and adversarial loss
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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 transformer for temporal prediction, validated on minimal channel flow simulations at \(Re_\tau = 200\). The extended analysis demonstrates that the model accurately predicts latent space dynamics, preserves chaotic characteristics, and enables accurate reconstruction of low-velocity streaks and dominant momentum transport mechanisms within one Lyapunov time.