4 papers
Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows
Xiao Xue, Tianyue Yang, Mingyang Gao +7
Spatiotemporal flows govern diverse phenomena across physics, biology, and engineering, yet modelling their multiscale dynamics remains a central challenge. Despite major advances…
Fast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
Xiao Xue, Marco F. P. ten Eikelder, Mingyang Gao +7
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle di…
Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model
Xiao Xue, Marco F. P. ten Eikelder, Tianyue Yang +4
Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical…
Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows
Xiao Xue, Shuo Wang, Hua-Dong Yao +2
Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and ae…