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20242026
most citedMulti-Scale Data Assimilation in Turbulent Models

1 citations · 1 across the 19 of their papers we have counts for

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physics.flu-dyn2026

Drift of interfaces in forced stably-stratified turbulence and the role of vertically-sheared helical structures

Niccolò Cocciaglia, Luca Biferale, Fabio Bonaccorso +1

Experimental investigations of forced stably stratified turbulence (SST) have shown that the step-like density profile, made of well-mixed density layers and sharp interfaces alter…

physics.flu-dyn2026

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…

physics.flu-dyn2026

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…

physics.flu-dyn2026

Smart strategies to navigate turbulent odor plumes reorienting to local wind

Lorenzo Piro, Maurizio Carbone, Luca Biferale +4

Olfactory search in turbulent environments is a sensorimotor problem that many animals solve with remarkable efficiency, yet replicating this ability in artificial systems is an en…

physics.flu-dyn2026

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,…

cs.RO2026

Olfactory pursuit: catching a moving odor source in complex flows

Maurizio Carbone, Lorenzo Piro, Robin A. Heinonen +3

Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental chal…

physics.flu-dyn2026

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…

physics.flu-dyn2026

Adaptive shape control for microswimmer navigation in turbulence

Jingran Qiu, Lorenzo Piro, Luca Biferale +3

Navigation in turbulent environments is a fundamental challenge for biological and artificial microswimmers. While most existing studies focus on adapting motility or steering, the…

physics.flu-dyn2026

On the importance of stochasticity in closures of turbulence

André Freitas, Luca Biferale, Mathieu Desbrun +3

Deterministic closures for coarse-grained turbulence models help reproduce mean statistics, but often fail to capture the finite-time growth of uncertainty. Using the framework of…