119 citations · 806 across the 42 of their papers we have counts for
7 papers · 1 filter
Multi-scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models
Tianyi Li, Alessandra S. Lanotte, Michele Buzzicotti +2
We address the problem of data augmentation in a rotating turbulence set-up, a paradigmatic challenge in geophysical applications. The goal is to reconstruct information in two-dim…
Data reconstruction for complex flows using AI: recent progress, obstacles, and perspectives
Michele Buzzicotti
In recent years the fluid mechanics community has been intensely focused on pursuing solutions to its long-standing open problems by exploiting the new machine learning, (ML), appr…
Global Cascade of Kinetic Energy in the Ocean and the Atmospheric Imprint
Benjamin A. Storer, Michele Buzzicotti, Hemant Khatri +2
We present the first estimate for the ocean's global scale-transfer of kinetic energy (KE), across scales from 10~km to 40000~km. We show the existence of oceanic KE transfer betwe…
Synthetic Lagrangian Turbulence by Generative Diffusion Models
Tianyi Li, Luca Biferale, Fabio Bonaccorso +2
Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans,…
TURB-Lagr. A database of 3d Lagrangian trajectories in homogeneous and isotropic turbulence
Luca Biferale, Fabio Bonaccorso, Michele Buzzicotti +1
We present TURB-Lagr, a new open database of 3d turbulent Lagrangian trajectories, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations in the pre…
Reconstructing Rayleigh-Benard flows out of temperature-only measurements using Physics-Informed Neural Networks
Patricio Clark Di Leoni, Lokahith Agasthya, Michele Buzzicotti +1
We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh-Benard flows using only temperature information. We perform a quantita…