Politecnico di Milano, Universitè de Liege 2022
Linear Eddy Viscosity Models, wingtip vortex, Large Eddy Simulations, Reynolds Average Navier-Stokes simulations.
1st International Symposium on AI and Fluid Mechanics (AiFluids 2025) 2025
Vortex-Induced Vibrations, Reduced-Order Models, Variational Autoencoder, Transformers, Generative Adversarial Networks, Turbulent Flows
Recommended citation: Tonioni, N., Agostini, L., Kerhervé, F., Cordier, L., & Vinuesa, R. (2025). VIVALDy: AI-Driven Low-Order Modeling of Vortex-Induced Vibrations via β-Variational Autoencoders, Transformers, and Adversarial Training. AiFluids 2025, Chania, Greece.
Download Paper
11th International Symposium on Turbulence Heat and Mass Transfer (THMT-25) 2025
Near-wall turbulence, Reduced-Order Models, Minimal channel, Transformers, Variational Autoencoder, Generative Adversarial Networks
Recommended citation: Tonioni, N., Umair, M., Agostini, L., Kerhervé, F., Cordier, L., & Vinuesa, R. (2025). Data-driven modeling of near-wall turbulence using β-variational autoencoder, transformers, and adversarial loss. THMT-25, Tokyo, Japan.
Download Paper
arXiv 2025
Machine-Learning, Deep Learning, Reduced-Order Models, Turbulent Flows, Vortex-Induced Vibrations
Recommended citation: Tonioni, N., Agostini, L., Cordier, L., & Vinuesa, R. (2025). VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations. arXiv preprint arXiv:2509.24965.
Download Paper
Physical Review Fluids 2026
Machine-Learning, Deep Learning, Reduced-Order Models, Turbulent Flows, Vortex-Induced Vibrations
Recommended citation: Tonioni, N., Agostini, L., Kerhervé, F., Cordier, L., & Vinuesa, R. (2026). VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations. Physical Review Fluids, 11, 044902.
Download Paper
Published:
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?
Published:
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.
Published:
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.
Published:
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.
Undergraduate course, Isae - École Nationale Supérieure De Mécanique Et D'aérotechnique, 2025
Responsibilities included: