3 papers
cs.LG2026
LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
Ze Tao, Hanxuan Wang, Fujun Liu
Physics-informed neural networks (PINNs) have attracted considerable attention for their ability to integrate partial differential equation priors into deep learning frameworks; ho…
physics.comp-ph2026
LSTM-PINN for Steady-State Electrothermal Transport: Preserving Multi-Field Consis tency in Strongly Coupled Heat and Fluid Flow
Yuqing Zhou, Ze Tao, Hanxuan Wang +1
Steady-state electrothermal systems involve strongly coupled heat transfer, fluid flow, and electric-potential transport, creating severe numerical challenges for standard physics-…
math-ph2025
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…