:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Yang, Wenqiang, Wu, Wenyuan, Feng, Yong, Chen, Changbo
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Analysis of PDEs
Accesso online:	https://arxiv.org/abs/2603.22724
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Embedding Method by Real Numerical Algebraic Geometry for Structurally Unamenable Differential-Algebraic Equations
di: Yang, Wenqiang, et al.
Pubblicazione: (2021)

Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
di: Nooraiepour, Mohammad, et al.
Pubblicazione: (2026)

Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
di: Gupta, Deepak, et al.
Pubblicazione: (2025)

Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
di: Koumpanakis, Michail, et al.
Pubblicazione: (2024)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
di: Andre-Sloan, Sebastien, et al.
Pubblicazione: (2026)

A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
di: Kathane, Shivprasad, et al.
Pubblicazione: (2024)

Solving the Poisson Equation with Dirichlet data by shallow ReLU$^α$-networks: A regularity and approximation perspective
di: Vaishampayan, Malhar, et al.
Pubblicazione: (2024)

Regularity of Second-Order Elliptic PDEs in Spectral Barron Spaces
di: Chen, Ziang, et al.
Pubblicazione: (2026)

Score Shocks: The Burgers Equation Structure of Diffusion Generative Models
di: Sarkar, Krisanu
Pubblicazione: (2026)

Latent Space Element Method
di: Chung, Seung Whan, et al.
Pubblicazione: (2026)

Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
di: Chen, Ziang, et al.
Pubblicazione: (2024)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
di: Hwang, Youngsik, et al.
Pubblicazione: (2024)

Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions
di: Chen, Ziang, et al.
Pubblicazione: (2025)

Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
di: Dai, Yilong, et al.
Pubblicazione: (2026)

SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
di: Su, Hua, et al.
Pubblicazione: (2025)

Gradient Flow Sampler-based Distributionally Robust Optimization
di: Xu, Zusen, et al.
Pubblicazione: (2025)

$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
di: Figueres, Júlia Vicens, et al.
Pubblicazione: (2025)

Power-Enhanced Residual Network for Function Approximation and Physics-Informed Inverse Problems
di: Noorizadegan, Amir, et al.
Pubblicazione: (2023)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
di: Chen, Chuqi, et al.
Pubblicazione: (2024)

A Machine Learning Approach to the Nirenberg Problem
di: Cortés, Gianfranco, et al.
Pubblicazione: (2026)

Hermite Neural Network Simulation for Solving the 2D Schrodinger Equation
di: Parand, Kourosh, et al.
Pubblicazione: (2024)

Learning functional components of PDEs from data using neural networks
di: Loman, Torkel E., et al.
Pubblicazione: (2026)

Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
di: Taniguchi, Koichi, et al.
Pubblicazione: (2026)

Is Zero-Shot Super-Resolution Possible in Operator Learning?
di: Subedi, Unique, et al.
Pubblicazione: (2026)

Regularity of Solutions to Beckmann's Parametric Optimal Transport
di: Gottschalk, Hanno, et al.
Pubblicazione: (2026)

Interaction-Force Transport Gradient Flows
di: Gladin, Egor, et al.
Pubblicazione: (2024)

Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds
di: Trillos, Nicolás García, et al.
Pubblicazione: (2025)

Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
di: Kaiser, Luis, et al.
Pubblicazione: (2024)

The emergence of clusters in self-attention dynamics
di: Geshkovski, Borjan, et al.
Pubblicazione: (2023)

Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery
di: Yu, Yue, et al.
Pubblicazione: (2024)

On Probabilistic Embeddings in Optimal Dimension Reduction
di: Murray, Ryan, et al.
Pubblicazione: (2024)

Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates
di: Mooney, Connor, et al.
Pubblicazione: (2024)

Adapting Noise to Data: Generative Flows from 1D Processes
di: Chemseddine, Jannis, et al.
Pubblicazione: (2025)

Improved Graph-based semi-supervised learning Schemes
di: Bozorgnia, Farid
Pubblicazione: (2024)

Kernel Approximation of Fisher-Rao Gradient Flows
di: Zhu, Jia-Jie, et al.
Pubblicazione: (2024)

Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
di: Zadgar, Arman, et al.
Pubblicazione: (2025)

Stiff Transfer Learning for Physics-Informed Neural Networks
di: Seiler, Emilien, et al.
Pubblicazione: (2025)

Trace Regularity PINNs: Enforcing $\mathrm{H}^{\frac{1}{2}}(\partial Ω)$ for Boundary Data
di: Kim, Doyoon, et al.
Pubblicazione: (2025)

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
di: Huang, Chih-Kang, et al.
Pubblicazione: (2026)

Unified generalization analysis for physics informed neural networks
di: Hashimoto, Yuka, et al.
Pubblicazione: (2026)