4 papers
Learnable Viscosity Modulation in Physics-Informed Neural Networks for Incompressible Flow Reconstruction
Ke Xu, Ze Tao, Fujun Liu
Accurately and stably solving the incompressible Navier--Stokes equations with physics-informed neural networks (PINNs) remains challenging, particularly for sparse or noisy observ…
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…
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…