5 papers · 1 filter
Shared Physics Responses Recover Hidden Rankings in Neural Operator Libraries
Hanbing Liang, Fujun Liu
Selecting the optimal neural-operator prediction during deployment is challenging when high-fidelity reference solutions are unavailable. We demonstrate that under a squared Hilber…
Wrong-Physics Backdoors in Neural PDE Operators
Hanbing Liang, Fujun Liu
Neural PDE operators are increasingly trained on reusable solver archives, yet validation often relies on clean prediction error and parameter-agnostic plausibility checks. We intr…
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…
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…
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…