Cover Image

Is the Frequency Principle always valid?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Zhai, Qijia
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Domain Decomposition Deep Neural Network Method with Multi-Activation Functions for Solving Elliptic and Parabolic Interface Problems
by: Zhai, Qijia
Published: (2025)

Discrete Differential Principle for Continuous Smooth Function Representation
by: Wang, Guoyou, et al.
Published: (2025)

Why Shallow Networks Struggle to Approximate and Learn High Frequencies
by: Zhang, Shijun, et al.
Published: (2023)

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
by: Tang, Kejun, et al.
Published: (2023)

A Probabilistic Framework for Solving High-Frequency Helmholtz Equations via Diffusion Models
by: Zou, Yicheng, et al.
Published: (2026)

Frequency-domain alignment of heterogeneous, multidimensional separations data through complex orthogonal Procrustes analysis
by: Armstrong, Michael Sorochan
Published: (2025)

Windowed Fourier Propagator: A Frequency-Local Neural Operator for Wave Equations in Inhomogeneous Media
by: Cai, Yiyang, et al.
Published: (2026)

Deep adaptive sampling for surrogate modeling without labeled data
by: Wang, Xili, et al.
Published: (2024)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions
by: Wang, Bo, et al.
Published: (2025)

A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
by: Chen, Zhenglong, et al.
Published: (2025)

Robust globally divergence-free HDG finite element method for steady thermally coupled incompressible MHD flow
by: Zhang, Min, et al.
Published: (2024)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
by: Berner, Julius, et al.
Published: (2025)

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
by: Mustajab, Abdul Hannan, et al.
Published: (2024)

Physics-informed neural networks for solving two-phase flow problems with moving interfaces
by: Zhai, Qijia, et al.
Published: (2026)

Maximum Principle of Optimal Probability Density Control
by: Gaby, Nathan, et al.
Published: (2025)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
by: Auroy, Samuel, et al.
Published: (2025)

A Neural Network for the Identical Kuramoto Equation: Architectural Considerations and Performance Evaluation
by: Panigrahi, Nishantak, et al.
Published: (2025)

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions
by: Lu, Yulong, et al.
Published: (2025)

Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
by: Carson, Erin, et al.
Published: (2025)

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation
by: Katende, Ronald
Published: (2025)

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
by: Zhou, Hanfei, et al.
Published: (2025)

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
by: Rowan, Conor, et al.
Published: (2025)

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
by: Blechschmidt, Jan, et al.
Published: (2025)

On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces
by: Iske, Armin
Published: (2025)

DPG loss functions for learning parameter-to-solution maps by neural networks
by: Castillo, Pablo Cortés, et al.
Published: (2025)

Learning to Solve Related Linear Systems
by: Hegde, Disha, et al.
Published: (2025)

chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data
by: Praveen, Harshwardhan, et al.
Published: (2025)

Leveraging Real-Time Data Analysis and Multiple Kernel Learning for Manufacturing of Innovative Steels
by: Rannetbauer, Wolfgang, et al.
Published: (2025)

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
by: Chuang, Pi-Yueh, et al.
Published: (2025)

A categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems
by: Hu, Wei-Fan, et al.
Published: (2025)

A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
by: Weihs, Adrien, et al.
Published: (2025)

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
by: Mao, Tong, et al.
Published: (2025)

Condition Numbers and Eigenvalue Spectra of Shallow Networks on Spheres
by: Liu, Xinliang, et al.
Published: (2025)

Learning solution operator of dynamical systems with diffusion maps kernel ridge regression
by: Song, Jiwoo, et al.
Published: (2025)

Integral regularization PINNs for evolution equations
by: Feng, Xiaodong, et al.
Published: (2025)

A Unified Approach to Analysis and Design of Denoising Markov Models
by: Ren, Yinuo, et al.
Published: (2025)

Auto-Adaptive PINNs with Applications to Phase Transitions
by: Buck, Kevin, et al.
Published: (2025)

SciML Agents: Write the Solver, Not the Solution
by: Gaonkar, Saarth, et al.
Published: (2025)

Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)