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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…
cs.LG2026
Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
Yuqing Zhou, Ze Tao, Fujun Liu
Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic energy harve…
cs.LG2025
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…