492 citations · 9.4k across the 216 of their papers we have counts for
120 papers · 1 filter
Intermittency suppression in turbulence via forced light particles
André Freitas, Xander M. de Wit, Ziqi Wang +2
We investigate how turbulence is reshaped by the presence of externally forced light particles, using high-resolution direct numerical simulations with four-way coupling. The parti…
Policy heterogeneity improves collective olfactory search in 3-D turbulence
Lorenzo Piro, Robin A. Heinonen, Maurizio Carbone +2
We investigate the role of policy heterogeneity in enhancing the olfactory search capabilities of cooperative agent swarms operating in complex, real-world turbulent environments.…
Exploring Bayesian olfactory search in realistic turbulent flows
Robin A. Heinonen, Luca Biferale, Antonio Celani +1
The problem of tracking the source of a passive scalar in a turbulent flow is relevant to flying insect behavior and several other applications. Extensive previous work has shown t…
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…
Enhanced Gas Source Localization Using Distributed IoT Sensors and Bayesian Inference
Leonardo Balocchi, Lorenzo Piro, Luca Biferale +5
Identifying a gas source in turbulent environments presents a significant challenge for critical applications such as environmental monitoring and emergency response. This issue is…
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…
Optimal trajectories for Bayesian olfactory search in turbulent flows: the low information limit and beyond
Robin A. Heinonen, Luca Biferale, Antonio Celani +1
In turbulent flows, tracking the source of a passive scalar cue requires exploiting the limited information that can be gleaned from rare, stochastic encounters with the cue. When…
Many wrong models approach to localize an odor source in turbulence with static sensors
Lorenzo Piro, Robin A. Heinonen, Massimo Cencini +1
The problem of locating an odor source in turbulent flows is central to key applications such as environmental monitoring and disaster response. We address this challenge by design…
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…
TURB-Hel: an open-access database of helically forced homogeneous and isotropic turbulence
Luca Biferale, Fabio Bonaccorso, Moritz Linkmann +1
We present TURB-Hel, a database formed by two datasets of incompressible homogeneous and isotropic turbulence, maintained in a statistically stationary state by fully helical forci…
Extreme statistics and extreme events in dynamical models of turbulence
Xander M. de Wit, Giulio Ortali, Alessandro Corbetta +3
We present a study of the intermittent properties of a shell model of turbulence with unprecedented statistics, about eddy turn over time, achieved thanks to an impleme…
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…
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning
Chiara Calascibetta, Luca Biferale, Francesco Borra +2
We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We…
Heavy and light inertial particle aggregates in homogeneous isotropic turbulence: A study on breakup and stress statistics
Graziano Frungieri, Matthaus U. Baebler, Luca Biferale +1
The breakup of inertial, solid aggregates in an incompressible, homogeneous and isotropic three-dimensional turbulent flow is studied by means of a direct numerical simulation, and…
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…