6 citations · 6 across the 15 of their papers we have counts for
Showing physics.flu-dynShow all
3 papers · 1 filter
physics.flu-dyn2026
High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
Baitong Zhou, Ze Tao, Ke Xu +2
Accurate reconstruction of localized extreme structures remains a critical bottleneck in the physics-informed modeling of electro-thermal-convective flows. Although conventional ph…
physics.flu-dyn2025★ 6 cited
LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow
Ze Tao, Ke Xu, Fujun Liu
Physics-Informed Neural Networks (PINNs) have demonstrated considerable success in solving complex fluid dynamics problems. However, their performance often deteriorates in regimes…
physics.flu-dyn2025
Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
Fulin Xing, Junjie Li, Ze Tao +2
Underwater explosion modeling faces a critical challenge of simultaneously resolving shock waves and gas-liquid interfaces, as traditional methods struggle to balance accuracy and…