37 citations · 37 across the 11 of their papers we have counts for
11 papers
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…
Super-resolution of satellite-derived SST data via Generative Adversarial Networks
Claudia Fanelli, Tiany Li, Luca Biferale +4
In this work, we address the super-resolution problem of satellite-derived sea surface temperature (SST) using deep generative models. Although standard gap-filling techniques are…
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…
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…
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…
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…