5 papers
Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling
Tianyue Yang, Sandro Tacchella, Xiao Xue
Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficien…
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…