Cover Image

Spectral Analysis of Hard-Constraint PINNs: The Spatial Modulation Mechanism of Boundary Functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xie, Yuchen, Chi, Honghang, Quan, Haopeng, Wang, Yahui, Wang, Wei, Ma, Yu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion
by: Chen, Nanxi, et al.
Published: (2025)

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs
by: Pisnoy, Shimon, et al.
Published: (2026)

Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
by: Shukla, Khemraj, et al.
Published: (2026)

Plane-Wave Decomposition and Randomised Training; a Novel Path to Generalised PINNs for SHM
by: Clements, Rory, et al.
Published: (2025)

Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations
by: Abedi, Mohammad Mahdi, et al.
Published: (2025)

Neural PDE Solvers with Physics Constraints: A Comparative Study of PINNs, DRM, and WANs
by: Chen, Jiakang
Published: (2025)

Mitigating Gradient Pathology in PINNs through Aligned Constraint
by: Luo, Yichen, et al.
Published: (2026)

An efficient hp-Variational PINNs framework for incompressible Navier-Stokes equations
by: Anandh, Thivin, et al.
Published: (2024)

Building Trust in PINNs: Error Estimation through Finite Difference Methods
by: Krasowski, Aleksander, et al.
Published: (2026)

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
by: Toscano, Juan Diego, et al.
Published: (2024)

Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
by: Chaudhry, Faris
Published: (2026)

Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
by: So, Chi Chiu, et al.
Published: (2024)

Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
by: Urbán, Jorge F., et al.
Published: (2024)

End-to-End Probabilistic Framework for Learning with Hard Constraints
by: Utkarsh, Utkarsh, et al.
Published: (2025)

The Hard-Constraint PINNs for Interface Optimal Control Problems
by: Lai, Ming-Chih, et al.
Published: (2023)

Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?
by: Zhou, Zijian, et al.
Published: (2024)

Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
by: Nikolaou, Konstantin, et al.
Published: (2026)

LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization
by: Zhou, Yuanye, et al.
Published: (2025)

Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
by: Gong, Yuhao, et al.
Published: (2024)

Auxiliary Finite-Difference Residual-Gradient Regularization for PINNs
by: Kassinos, Stavros
Published: (2026)

Empirical Stability Analysis of Kolmogorov-Arnold Networks in Hard-Constrained Recurrent Physics-Informed Discovery
by: Spotorno, Enzo Nicolas, et al.
Published: (2026)

A phase transition in sampling from Restricted Boltzmann Machines
by: Kwon, Youngwoo, et al.
Published: (2024)

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
by: Jiao, Yuling, et al.
Published: (2023)

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
by: Luo, Erpai, et al.
Published: (2025)

Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module
by: Oh, Yun-Tak, et al.
Published: (2026)

Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
by: Wang, Yanbo, et al.
Published: (2024)

Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Space
by: Xu, Zemin, et al.
Published: (2025)

Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases
by: Cooley, Madison, et al.
Published: (2024)

The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
by: Wang, Yuhao, et al.
Published: (2025)

Discovering Decoupled Functional Modules in Large Language Models
by: Yu, Yanke, et al.
Published: (2026)

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
by: White, Alistair, et al.
Published: (2023)

PINNs in More General Geometry
by: Hirst, Edward
Published: (2026)

FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
by: Wang, Sifan, et al.
Published: (2025)

GeoFunFlow: Geometric Function Flow Matching for Inverse Operator Learning over Complex Geometries
by: Wang, Sifan, et al.
Published: (2025)

DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
by: Li, Jiayi, et al.
Published: (2025)

Gradient Scaling Effects in Adaptive Spectral PINNs for Stiff Nonlinear ODEs
by: Yepes, Isabela M., et al.
Published: (2026)

LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
by: Wang, Jing, et al.
Published: (2025)

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
by: Sobh, Nahil, et al.
Published: (2025)

BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions
by: Nair, Ashish S., et al.
Published: (2026)

Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
by: Khodakarami, Siavash, et al.
Published: (2025)