492 citations · 9.4k across the 216 of their papers we have counts for
9 papers · 2 filters
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…
Instanton-based Importance Sampling for Extreme Fluctuations in a Shell Model for Turbulent Energy Cascade
Guilherme Tegoni Goedert, Luca Biferale
Many out-of-equilibrium flows present non-Gaussian fluctuations in physically relevant observables, such as energy dissipation rate. This implies extreme fluctuations that, althoug…
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,…
Optimal tracking strategies in a turbulent flow
Chiara Calascibetta, Luca Biferale, Francesco Borra +2
Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the searcher has limited propulsion and maneuvering capabilities. Even in the case when the r…
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…
Ductile Breakup of Tracer Aggregates in Homogenous Isotropic Turbulence
Graziano Frungieri, Matthaus U. Babler, Luca Biferale +1
In this paper we study the ductile breakup of tracer aggregates in an incompressible, homogeneous, and isotropic three-dimensional turbulent flow. The flow dynamics is studied by m…
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…
New Exact Betchov-like Relation for the Helicity Flux in Homogeneous Turbulence
Damiano Capocci, Perry L. Johnson, Sean Oughton +2
In homogeneous and isotropic turbulence, the relative contributions of different physical mechanisms to the energy cascade can be quantified by an exact decomposition of the energy…
Generative Adversarial Networks to infer velocity components in rotating turbulent flows
Tianyi Li, Michele Buzzicotti, Luca Biferale +1
Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstructio…