Image de couverture de livre

Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Celaya, Adrian, Wang, Yimo, Fuentes, David, Riviere, Beatrice
Format:	Preprint
Publié:	2025
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

Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
par: Celaya, Adrian, et autres
Publié: (2023)

Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
par: Celaya, Adrian, et autres
Publié: (2025)

The Immersed Discontinuous Galerkin Method for Elliptic Interface Problems
par: Yang, Lin, et autres
Publié: (2026)

A Multiple Transferable Neural Network Method with Domain Decomposition for Elliptic Interface Problems
par: Lu, Tianzheng, et autres
Publié: (2025)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
par: Dai, Yongcheng, et autres
Publié: (2023)

A Hybridizable Discontinuous Galerkin Method for the Miscible Displacement Problem Under Minimal Regularity
par: Kirk, Keegan L. A., et autres
Publié: (2025)

Preconditioned NonSymmetric/Symmetric Discontinuous Galerkin Method for Elliptic Problem with Reconstructed Discontinuous Approximation
par: Li, Ruo, et autres
Publié: (2023)

Analysis of a Discontinuous Galerkin Method for Diffusion Problems on Intersecting Domains
par: Kuchta, Miroslav, et autres
Publié: (2025)

An Iterative Deep Ritz Method for Monotone Elliptic Problems
par: Hu, Tianhao, et autres
Publié: (2025)

Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
par: Bruna, Joan, et autres
Publié: (2022)

A Neural-Enhanced Weak Galerkin Method for Second-Order Elliptic Problems with Low-Regularity Solutions
par: Wang, Chunmei
Publié: (2026)

Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
par: Chen, Jie
Publié: (2024)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
par: Zhang, Borong, et autres
Publié: (2022)

Proximal Discontinuous Galerkin Methods for Variational Inequalities
par: Ern, Alexandre, et autres
Publié: (2026)

Parallel-in-Time Solutions with Random Projection Neural Networks
par: Betcke, Marta M., et autres
Publié: (2024)

Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves
par: Heaney, Claire E., et autres
Publié: (2020)

Deep Neural Network Solutions for Oscillatory Fredholm Integral Equations
par: Jiang, Jie, et autres
Publié: (2024)

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
par: Saporiti, Riccardo, et autres
Publié: (2026)

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

Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
par: Zhao, Yuxuan, et autres
Publié: (2026)

Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
par: Yang, Mengxi, et autres
Publié: (2022)

Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
par: Burman, Erik, et autres
Publié: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
par: Mehmood, Muhammed Ali, et autres
Publié: (2026)

Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
par: Trifonov, Vladislav, et autres
Publié: (2024)

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)

N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
par: Baek, Jonghyuk, et autres
Publié: (2024)

Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins
par: Ning, Zhiyang, et autres
Publié: (2025)

A Dimensionality Reduction Approach for Convolutional Neural Networks
par: Meneghetti, Laura, et autres
Publié: (2021)

A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
par: Celaya, Adrian, et autres
Publié: (2023)

Quadratic Discontinuous Galerkin methods for Unilateral Contact Problem
par: Porwal, Kamana, et autres
Publié: (2024)

Stabilizing and Solving Unique Continuation Problems by Parameterizing Data and Learning Finite Element Solution Operators
par: Burman, Erik, et autres
Publié: (2024)

Learn Singularly Perturbed Solutions via Homotopy Dynamics
par: Chen, Chuqi, et autres
Publié: (2025)

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

Latent Neural Operator for Solving Forward and Inverse PDE Problems
par: Wang, Tian, et autres
Publié: (2024)

Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
par: Zhao, Xuanle, et autres
Publié: (2023)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
par: Lei, Guanhang, et autres
Publié: (2023)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
par: Sahin, T., et autres
Publié: (2023)

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

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
par: Zhou, Wei, et autres
Publié: (2024)

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
par: Jentzen, Arnulf, et autres
Publié: (2023)