119 citations · 806 across the 42 of their papers we have counts for
37 papers · 1 filter
Gappy Reconstruction of Bubbly Flows by Guided Diffusion Models
Hridey Narula, Tianyi Li, Michele Buzzicotti +2
Experiments in multiphase flows are often limited in their ability to simultaneously obtain velocity measurements in different phases. At the same time, flow reconstruction from ph…
Stochastic Multiscale Reconstruction of Lagrangian Turbulence via Guided Diffusion Models
Conghui Wang, Tianyi Li, Luca Biferale +3
Lagrangian turbulence is characterized by intermittent, fat-tailed fluctuations and nontrivial correlations across temporal scales, making a quantitative description of its full mu…
Turbulent pair dispersion with Stochastic Generative Diffusion Models
Andrei Pantea, Luca Biferale, Michele Buzzicotti +3
Recent advances in data-driven modeling have shown that diffusion models can successfully generate synthetic Lagrangian trajectories in turbulent flows. Building on this progress,…
Physics-Constrained Diffusion Model for Synthesis of 3D Turbulent Data
Tianyi Li, Michele Buzzicotti, Fabio Bonaccorso +1
Synthesizing fully developed three-dimensional turbulent velocity fields remains a long-standing problem in fluid mechanics and an open challenge for generative modeling. The diffi…
Inferring the Turbulent Breakup of Colloidal Aggregates Using Graph Neural Networks
Michele Buzzicotti, Massimo Cencini, Giulio Cimini +2
Solid aggregates in turbulent suspensions may break under the action of shear stresses. We explore the use of Graph Neural Networks (GNN) to infer aggregate fragmentation once the…
Deterministic diffusion models for Lagrangian turbulence: robustness and encoding of extreme events
Tianyi Li, Flavio Tuteri, Michele Buzzicotti +2
Modeling Lagrangian turbulence remains a fundamental challenge due to its multiscale, intermittent, and non-Gaussian nature. Recent advances in data-driven diffusion models have en…
Scale invariance of intermittency in LES turbulence
B. Magacho, S. Thalabard, M. Buzzicotti +3
Turbulent flows exhibit large intermittent fluctuations from inertial to dissipative scales, characterized by multifractal statistics and breaking the statistical self-similarity.…
A posteriori closure of turbulence models: are symmetries preserved?
André Freitas, Kiwon Um, Mathieu Desbrun +2
Turbulence modeling remains a longstanding challenge in fluid dynamics. Recent advances in data-driven methods have led to a surge of novel approaches aimed at addressing this prob…
Generation of cosmic ray trajectories by a Diffusion Model trained on test particles in 3D magnetohydrodynamic turbulence
Johannes Martin, Jeremiah Lübke, Tianyi Li +3
Models for the transport of high energy charged particles through strong magnetic turbulence play a key role in space and astrophysical studies, such as describing the propagation…
Solver-in-the-loop approach to closure of shell models of turbulence
André Freitas, Kiwon Um, Mathieu Desbrun +2
This work studies an a posteriori data-driven approach (known as solver-in-the-loop) for sub-grid modeling of a shell model for turbulence. This approach takes advantage of the dif…
Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models
Tianyi Li, Luca Biferale, Fabio Bonaccorso +2
We present a stochastic method for reconstructing missing spatial and velocity data along the trajectories of small objects passively advected by turbulent flows with a wide range…
Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence
Tianyi Li, Samuele Tommasi, Michele Buzzicotti +2
Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flow…
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…
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…
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…
Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks
Tianyi Li, Michele Buzzicotti, Luca Biferale +3
Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools. This problem is crucial for numerous geophysical applications and fundamental aspec…
Classifying Turbulent Environments via Machine Learning
Michele Buzzicotti, Fabio Bonaccorso
The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e…
Inertial range statistics of the Entropic Lattice Boltzmann in 3D turbulence
Michele Buzzicotti, Guillaume Tauzin
We present a quantitative analysis of the inertial range statistics produced by Entropic Lattice Boltzmann Method (ELBM) in the context of 3d homogeneous and isotropic turbulence.…
Synchronizing subgrid scale models of turbulence to data
M. Buzzicotti, P. Clark Di Leoni
Large Eddy Simulations of turbulent flows are powerful tools used in many engineering and geophysical settings. Choosing the right value of the free parameters for their subgrid sc…
TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows
L. Biferale, F. Bonaccorso, M. Buzzicotti +1
We present TURB-Rot, a new open database of 3d and 2d snapshots of turbulent velocity fields, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations…
Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
M. Buzzicotti, F. Bonaccorso, P. Clark Di Leoni +1
We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulenc…