18 papers
Exact ballistic energy transport and emergent XXZ dynamics in an integrable three-state chain
Hanbing Liang, Fujun Liu
We investigate the coupling dependence of ballistic energy transport and the emergent spin dynamics in an integrable Hermitian three-state chain that connects a clock interaction t…
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…
Spatio-Temporal Uncertainty-Modulated Physics-Informed Neural Networks for Solving Hyperbolic Conservation Laws with Strong Shocks
Darui Zhao, Ze Tao, Fujun Liu
Physics-Informed Neural Networks (PINNs) frequently encounter difficulties in accurately resolving shock waves within high-speed compressible flows, a failure largely attributed to…
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-…
Bias Inheritance in Neural-Symbolic Discovery of Constitutive Closures Under Function-Class Mismatch
Hanbing Liang, Ze Tao, Fujun Liu
We investigate the data-driven discovery of constitutive closures in nonlinear reaction-diffusion systems with known governing PDE structures. Our objective is to robustly recover…
Macroscopic transport patterns of UAV traffic in 3D anisotropic wind fields: A constraint-preserving hybrid PINN-FVM approach
Hanbing Liang, Fujun Liu
Macroscopic unmanned aerial vehicle (UAV) traffic organization in three-dimensional airspace faces significant challenges from static wind fields and complex obstacles. A critical…
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…
PICS: A Partition-of-unity Information-geometric Certified Solver for Coupled Partial Differential Equations
Ze Tao, Hongfu Zhou, Hanbing Liang +1
Coupled partial differential equations underpin a wide range of multiphysics systems, yet existing neural PDE solvers still struggle to resolve localized high-risk regions and ofte…
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…
A Residual-Attention Physics-Informed Neural Network for Irregular Interfaces and Multi-Peak Transport Fields
Baitong Zhou, Ze Tao, Fujun Liu +1
In complex engineering systems such as electro-thermal-fluid coupling, rapid and accurate prediction of multi-physics fields is essential for advanced applications like digital twi…
A Unified Benchmark Study of Shock-Like Problems in Two-Dimensional Steady Electrohydrodynamic Flow Based on LSTM-PINN
Chao Lin, Ze Tao, Fujun Liu
Accurately resolving steady electrohydrodynamic (EHD) flows presents a formidable computational challenge due to the strong nonlinear coupling between charged-particle density, vel…
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…
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…