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