6 citations · 6 across the 6 of their papers we have counts for
7 papers
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…
Integrable construction of a two-dimensional lattice model with anisotropic Hubbard couplings
Ze Tao, Fujun Liu
By defining a graded global R-operator that couples free-fermion structures and incorporates anisotropic Hubbard interactions while satisfying the Yang-…
xLSTM-PINN: Memory-Gated Spectral Remodeling for Physics-Informed Learning
Ze Tao, Darui Zhao, Fujun Liu +2
Physics-informed neural networks (PINN) face significant challenges from spectral bias, which impedes their ability to model high-frequency phenomena and limits extrapolation perfo…
Operator-Consistent Physics-Informed Learning for Wafer Thermal Reconstruction in Lithography
Ze Tao, Fujun Liu, Yuxi Jin +6
Thermal field reconstruction in post-exposure bake (PEB) is critical for advanced lithography, yet current physics-informed neural networks (PINNs) suffer from inconsistent accurac…
A Unified Symmetry-Constrained Framework for Band Inversions in Photonic Crystals with Symmetry
Ze Tao, Fujun Liu
The lack of a unified theoretical framework for characterizing band inversions across different crystal symmetries hinders the rapid development of topological photonic band engine…
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…