119 citations · 446 across the 12 of their papers we have counts for
12 papers
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…
Generation of Turbulent States using Physics-Informed Neural Networks
Sofia Angriman, Pablo Cobelli, Pablo Mininni +2
When modelling turbulent flows, it is often the case that information on the forcing terms or the boundary conditions is either not available or overly complicated and expensive to…
Reconstructing Rayleigh-Bénard flows out of temperature-only measurements using nudging
Lokahith Agasthya, Patricio Clark Di Leoni, Luca Biferale
Nudging is a data assimilation technique that has proved to be capable of reconstructing several highly turbulent flows from a set of partial spatiotemporal measurements. In this s…
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning
Michele Buzzicotti, Luca Biferale, Fabio Bonaccorso +2
We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid under realistic…
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…
Phase transitions and flux-loop metastable states in rotating turbulence
P. Clark Di Leoni, A. Alexakis, L. Biferale +1
By using direct numerical simulations of up to a record resolution of 512x512x32768 grid points we discover the existence of a new metastable out-of-equilibrium state in rotating t…
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning
Luca Biferale, Fabio Bonaccorso, Michele Buzzicotti +2
To find the path that minimizes the time to navigate between two given points in a fluid flow is known as Zermelo's problem. Here, we investigate it by using a Reinforcement Learni…
Synchronization to big-data: nudging the Navier-Stokes equations for data assimilation of turbulent flows
P. Clark Di Leoni, A. Mazzino, L. Biferale
Nudging is an important data assimilation technique where partial field measurements are used to control the evolution of a dynamical system and/or to reconstruct the entire phase-…
Quantitative estimation of effective viscosity in quantum turbulence
Vishwanath Shukla, Pablo D. Mininni, Giorgio Krstulovic +2
We study freely decaying quantum turbulence by performing high resolution numerical simulations of the Gross-Pitaevskii equation (GPE) in the Taylor-Green geometry. We use resoluti…
Inferring flow parameters and turbulent configuration with physics-informed data-assimilation and spectral nudging
P. Clark Di Leoni, A. Mazzino, L. Biferale
Inferring physical parameters of turbulent flows by assimilation of data measurements is an open challenge with key applications in meteorology, climate modeling and astrophysics.…
Dual cascade and dissipation mechanisms in helical quantum turbulence
P. Clark di Leoni, P. D. Mininni, M. E. Brachet
While in classical turbulence helicity depletes nonlinearity and can alter the evolution of turbulent flows, in quantum turbulence its role is not fully understood. We present nume…