:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Dai, Yilong, Chen, Shengyu, Wang, Ziyi, Jia, Xiaowei, Xie, Yiqun, Kumar, Vipin, Yu, Runlong
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Analysis of PDEs
Acceso en línea:	https://arxiv.org/abs/2601.14517
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
por: Dai, Yilong, et al.
Publicado: (2026)

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

Convergence of Physics-Informed Neural Networks for Fully Nonlinear PDE's
por: Arakelyan, Avetik, et al.
Publicado: (2024)

Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
por: Dai, Yilong, et al.
Publicado: (2026)

Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
por: Mukherjee, Amartya, et al.
Publicado: (2026)

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

A PDE Perspective on Approximating Nonlocal Periodic Operators with Applications on Neural Networks for Critical SQG Equations
por: Abdo, Elie, et al.
Publicado: (2024)

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
por: Kavousanakis, Michail, et al.
Publicado: (2025)

Investigations on Physics-Informed Neural Networks for Aerodynamics
por: Coulaud, Guillaume, et al.
Publicado: (2024)

A detailed and comprehensive account of fractional Physics-Informed Neural Networks: From implementation to efficiency
por: Dabiri, Donya, et al.
Publicado: (2025)

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
por: Li, Zijie, et al.
Publicado: (2024)

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

Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network
por: Xie, Jiayi, et al.
Publicado: (2024)

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

Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
por: Eshaghi, Mohammad Sadegh, et al.
Publicado: (2024)

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

Solving Newell-Whitehead-Segel and Allen-Cahn Equations Employing Physics-Informed Neural Networks: A Comparative Analysis with Spline Methods
por: Shah, Ali Haider, et al.
Publicado: (2025)

A Physics-informed Multi-resolution Neural Operator
por: Roy, Sumanta, et al.
Publicado: (2025)

Neural Tangent Kernel Analysis to Probe Convergence in Physics-informed Neural Solvers: PIKANs vs. PINNs
por: Faroughi, Salah A., et al.
Publicado: (2025)

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

Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
por: Farkane, Ayoub, et al.
Publicado: (2026)

Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers
por: Haight, Chandler, et al.
Publicado: (2025)

Physics-Informed Neural Networks for Solving Forward and Inverse PDEs with Limited and Noisy Data: Application to Solar Corona Modeling
por: Baty, Hubert
Publicado: (2025)

Neural Operators for PDE Backstepping Control of First-Order Hyperbolic PIDE with Recycle and Delay
por: Qi, Jie, et al.
Publicado: (2023)

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

Parameter Estimation and Adaptive Solution of the Leray-Burgers Equation Using Physics-Informed Neural Networks
por: Kim, Bong-Sik, et al.
Publicado: (2023)

A Survey of Foundation Models for Environmental Science
por: Yu, Runlong, et al.
Publicado: (2025)

Existence of Solutions to $L_p$-Gaussian Minkowski problem
por: Tang, Shengyu
Publicado: (2023)

Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics
por: Li, Haolin, et al.
Publicado: (2024)

Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
por: Gaynutdinova, Liya, et al.
Publicado: (2025)

Error bounds for Physics Informed Neural Networks in Nonlinear Schrödinger equations placed on unbounded domains
por: Alejo, Miguel Á., et al.
Publicado: (2024)

Orbital stability of solitary waves for the Schr odinger-Boussinesq system
por: Ma, Yilong, et al.
Publicado: (2025)

On a PDE model for Learning in Stochastic Market Entry Games
por: Dagher, Esther Bou, et al.
Publicado: (2026)

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
por: Kumar, Dibyakanti, et al.
Publicado: (2023)

$L_p$ Minkowski problem and Brunn-Minkowski inequality for dual quermassintegrals
por: Chen, Xiaojuan, et al.
Publicado: (2026)

On the approximation of spatial convolutions by PDE systems
por: Ishii, Hiroshi, et al.
Publicado: (2024)

Coupling Physics Informed Neural Networks with External Solvers
por: Halder, Rahul, et al.
Publicado: (2025)

Bulk-Surface Coupled PDE with an Open Boundary
por: Epstein, Charles L., et al.
Publicado: (2026)

Adjoint Method in PDE-based Image Compression
por: Belhachmi, Zakaria, et al.
Publicado: (2023)

Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems
por: Xia, Mingtao, et al.
Publicado: (2022)