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…
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…
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…