3 papers
cs.LG2026
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
Daniele Berardini, Vito Paolo Pastore, Vittorio Murino
One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data d…
physics.flu-dyn2026
Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao +1
We present a physics-constrained, data-driven subgrid-scale (SGS) stress closure for large-eddy simulation (LES) in the lattice Boltzmann method (LBM). Trained on filtered-downsamp…
physics.flu-dyn2004
Retarded Many-Sphere Hydrodynamic Interactions in a Viscous Fluid
P. P. J. M. Schram, A. S. Usenko, I. P. Yakimenko
An alternative method is suggested for the description of the velocity and pressure fields in an unbounded incompressible viscous fluid induced by an arbitrary number of spheres mo…