3 papers
physics.comp-ph2026
Discontinuity-aware KAN-based physics-informed neural networks
Guoqiang Lei, D. Exposito, Xuerui Mao
Physics-informed neural networks (PINNs) have proven to be a promising method for the rapid solving of partial differential equations (PDEs) in both forward and inverse problems. H…
physics.comp-ph2026
Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
Guoqiang Lei, Zhihua Wang, Lijing Zhou +2
Physics-informed neural networks (PINNs) have been applied to simulate multiphase flows, yet they are limited in modeling phase changes and sharp interfaces due to optimization con…
physics.flu-dyn2024
An energy-stable phase-field model for droplet icing simulations
Zhihua Wang, Lijing Zhou, Wenqiang Zhang +3
A phase-field model for three-phase flows is established by combining the Navier-Stokes (NS) and the energy equations, with the Allen-Cahn (AC) and Cahn-Hilliard (CH) equations and…