Image de couverture de livre

Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Park, Yesom, Song, Changhoon, Kang, Myungjoo
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Numerical Analysis
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
par: Kim, Taeyoung, et autres
Publié: (2024)

Localized Estimation of Condition Numbers for MILU Preconditioners on a Graph
par: Hwang, Geonho, et autres
Publié: (2024)

Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
par: Park, Yesom, et autres
Publié: (2025)

RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
par: Song, Zhenao
Publié: (2025)

Accelerating Fractional PINNs using Operational Matrices of Derivative
par: Taheri, Tayebeh, et autres
Publié: (2024)

Sobolev Approximation of Deep ReLU Networks in Log-Barron Space
par: Song, Changhoon, et autres
Publié: (2026)

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

Integral regularization PINNs for evolution equations
par: Feng, Xiaodong, et autres
Publié: (2025)

Understanding the Difficulty of Solving Cauchy Problems with PINNs
par: Wang, Tao, et autres
Publié: (2024)

Auto-Adaptive PINNs with Applications to Phase Transitions
par: Buck, Kevin, et autres
Publié: (2025)

E-PINNs: Epistemic Physics-Informed Neural Networks
par: Jacob, Bruno, et autres
Publié: (2025)

Multi-Preconditioned LBFGS for Training Finite-Basis PINNs
par: Salvadó-Benasco, Marc, et autres
Publié: (2026)

Scalable Fixed-Point Framework for High-Dimensional Hamilton-Jacobi Equations
par: Park, Yesom, et autres
Publié: (2026)

Using Parametric PINNs for Predicting Internal and External Turbulent Flows
par: Ghosh, Shinjan, et autres
Publié: (2024)

TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
par: Chen, Yanlai, et autres
Publié: (2024)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
par: Auroy, Samuel, et autres
Publié: (2025)

High precision PINNs in unbounded domains: application to singularity formulation in PDEs
par: Wang, Yixuan, et autres
Publié: (2025)

Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
par: Disarò, Sean, et autres
Publié: (2026)

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
par: Bioli, Ivan, et autres
Publié: (2025)

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
par: Huang, Chih-Kang, et autres
Publié: (2026)

PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
par: Shi, Shengzhu, et autres
Publié: (2023)

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
par: Hillebrecht, Birgit, et autres
Publié: (2022)

Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
par: Rowan, Conor, et autres
Publié: (2025)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
par: Liang, Zhangyong
Publié: (2026)

PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces
par: Gao, Jiexing, et autres
Publié: (2023)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
par: Gao, Yihang, et autres
Publié: (2022)

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
par: Kumar, Dibyakanti, et autres
Publié: (2023)

KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers
par: Bounja, Karim, et autres
Publié: (2025)

Geometry-aware PINNs for Turbulent Flow Prediction
par: Ghosh, Shinjan, et autres
Publié: (2024)

Neural Shortest Path for Surface Reconstruction from Point Clouds
par: Park, Yesom, et autres
Publié: (2025)

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
par: Zhuang, Bozhou, et autres
Publié: (2024)

Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
par: Ren, Mohan, et autres
Publié: (2024)

Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
par: Fang, Ronglong, et autres
Publié: (2025)

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
par: Liu, Jerry, et autres
Publié: (2025)

Convergence Analysis of PINNs for Fractional Diffusion Equations in Bounded Domains
par: Abdo, Elie, et autres
Publié: (2026)

Sharp Convergence Rates for Matching Pursuit
par: Klusowski, Jason M., et autres
Publié: (2023)

Convergence Analysis of Probability Flow ODE for Score-based Generative Models
par: Huang, Daniel Zhengyu, et autres
Publié: (2024)

Convergence analysis of OT-Flow for sample generation
par: Jing, Yang, et autres
Publié: (2024)

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

An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training
par: Kusch, Jonas, et autres
Publié: (2025)