41 citations · 46 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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-…
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…