Image de couverture de livre

Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Katende, Ronald
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Numerical Analysis Functional Analysis
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

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

Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
par: Katende, Ronald
Publié: (2024)

Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
par: Mango, John, et autres
Publié: (2024)

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

Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
par: Katende, Ronald
Publié: (2024)

How Analysis Can Teach Us the Optimal Way to Design Neural Operators
par: Le, Vu-Anh, et autres
Publié: (2024)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
par: Berner, Julius, et autres
Publié: (2025)

Quantum Harmonic Analysis and the Structure in Data: Augmentation
par: Doerfler, Monika, et autres
Publié: (2025)

Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
par: Chang, Che-Chia, et autres
Publié: (2026)

A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
par: Mango, John M., et autres
Publié: (2025)

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
par: Yang, Jia-Qi, et autres
Publié: (2025)

Physics-Informed Geometry-Aware Neural Operator
par: Zhong, Weiheng, et autres
Publié: (2024)

Optimal lower Lipschitz bounds for ReLU layers, saturation, and phase retrieval
par: Freeman, Daniel, et autres
Publié: (2025)

Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs
par: Zhao, Xuxiang, et autres
Publié: (2026)

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

Construction of generalized samplets in Banach spaces
par: Balazs, Peter, et autres
Publié: (2024)

Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
par: Tu, Renbo, et autres
Publié: (2023)

Preconditioning for Physics-Informed Neural Networks
par: Liu, Songming, et autres
Publié: (2024)

Core-Conditioned Regularized Matrix Tri-Factorization for High-Dimensional Structured Systems
par: Katende, Ronald
Publié: (2024)

A Nonlinear Generalization of the Bauer-Fike Theorem and Novel Iterative Methods for Solving Nonlinear Eigenvalue Problems
par: Katende, Ronald
Publié: (2024)

Dual-Balancing for Physics-Informed Neural Networks
par: Zhou, Chenhong, et autres
Publié: (2025)

Universal Approximation of Nonlinear Operators and Their Derivatives
par: de Feo, Filippo
Publié: (2026)

Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy
par: Lu, Yiping, et autres
Publié: (2024)

Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
par: Wei, Zhi-Feng, et autres
Publié: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
par: Gonon, Lukas, et autres
Publié: (2024)

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

WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
par: Zhu, Bokai, et autres
Publié: (2026)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
par: Andre-Sloan, Sebastien, et autres
Publié: (2026)

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

Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
par: Farkane, Ayoub, et autres
Publié: (2026)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
par: Mohammad-Djafari, Ali
Publié: (2025)

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
par: Guan, Jiajing, et autres
Publié: (2024)

Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
par: Matsubara, Takashi, et autres
Publié: (2023)

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
par: Kratsios, Anastasis, et autres
Publié: (2025)

From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
par: Yang, Jianan, et autres
Publié: (2026)

Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
par: Cheng, Xiaoran, et autres
Publié: (2024)

Asymptotic behavior of polynomials orthonormal on a homogeneous set
par: Peherstorfer, Franz, et autres
Publié: (2002)

Operator SVD with Neural Networks via Nested Low-Rank Approximation
par: Ryu, J. Jon, et autres
Publié: (2024)

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
par: Lotfalian, Amin, et autres
Publié: (2025)

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
par: Yu, Yifan, et autres
Publié: (2025)