Imagen de Portada

Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Herb, Julius, Fritzen, Felix
Formato:	Preprint
Publicado:	2025
Materias:	Numerical Analysis Machine Learning 65
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Accelerating Conjugate Gradient Solvers for Homogenization Problems With Unitary Neural Operators
por: Julius Herb, et al.
Publicado: (2026)

A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers
por: Lee, Youngkyu, et al.
Publicado: (2025)

Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
por: Kopaničáková, Alena, et al.
Publicado: (2025)

Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
por: Sittoni, Pietro, et al.
Publicado: (2026)

Learning Sparse Approximate Inverse Preconditioners for Conjugate Gradient Solvers on GPUs
por: Yang, Zherui, et al.
Publicado: (2025)

Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
por: Trifonov, Vladislav, et al.
Publicado: (2024)

Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES
por: van Gastelen, Toby, et al.
Publicado: (2025)

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
por: Arisaka, Sohei, et al.
Publicado: (2024)

Approximation of the Proximal Operator of the $\ell_\infty$ Norm Using a Neural Network
por: Linehan, Kathryn, et al.
Publicado: (2024)

Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
por: Zhang, Enrui, et al.
Publicado: (2022)

INEUS: Iterative Neural Solver for High-Dimensional PIDEs
por: Dupret, Jean-Loup, et al.
Publicado: (2026)

Function Extrapolation with Neural Networks and Its Application for Manifolds
por: Hay, Guy, et al.
Publicado: (2024)

Efficient Shallow Ritz Method For 1D Diffusion-Reaction Problems
por: Cai, Zhiqiang, et al.
Publicado: (2024)

Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
por: Chen, Chuqi, et al.
Publicado: (2024)

Performance of Neural and Polynomial Operator Surrogates
por: Westermann, Josephine, et al.
Publicado: (2026)

Walk-on-Interfaces: A Monte Carlo Estimator for an Elliptic Interface Problem with Nonhomogeneous Flux Jump Conditions and a Neumann Boundary Condition
por: Ding, Xinwen, et al.
Publicado: (2025)

A Structure-Guided Gauss-Newton Method for Shallow ReLU Neural Network
por: Cai, Zhiqiang, et al.
Publicado: (2024)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
por: Li, Rui, et al.
Publicado: (2024)

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks
por: Shan, Tao, et al.
Publicado: (2025)

Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks
por: Gergő, Ungvári, et al.
Publicado: (2026)

Numerical Verification of PolyDG Algebraic Solvers for the Pseudo-Stress Stokes Problem
por: Antonietti, Paola F., et al.
Publicado: (2025)

Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization
por: Yao, Boyuan, et al.
Publicado: (2025)

Latent Neural Operator for Solving Forward and Inverse PDE Problems
por: Wang, Tian, et al.
Publicado: (2024)

Residual Multi-Fidelity Neural Network Computing
por: Davis, Owen, et al.
Publicado: (2023)

Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
por: Feng, Xue, et al.
Publicado: (2026)

Recurrent Neural Operators: Stable Long-Term PDE Prediction
por: Ye, Zaijun, et al.
Publicado: (2025)

A Numerical Analysis of Sketched Linear Squares Problems and Stopping Criteria for Iterative Solvers
por: Jia, Zhongxiao, et al.
Publicado: (2026)

Discretization Error of Fourier Neural Operators
por: Lanthaler, Samuel, et al.
Publicado: (2024)

Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
por: Roy, Hrittik, et al.
Publicado: (2025)

How Can Deep Neural Networks Fail Even With Global Optima?
por: Guan, Qingguang
Publicado: (2024)

Stabilizing PDE--ML coupled systems
por: Qadeer, Saad, et al.
Publicado: (2025)

Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
por: Tsyganov, Askar, et al.
Publicado: (2025)

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
por: De Ryck, Tim, et al.
Publicado: (2024)

Uncertainty quantification for deeponets with ensemble kalman inversion
por: Pensoneault, Andrew, et al.
Publicado: (2024)

Improving the accuracy of physics-informed neural networks via last-layer retraining
por: Qadeer, Saad, et al.
Publicado: (2026)

Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions
por: He, Juncai, et al.
Publicado: (2023)

PODNO: Proper Orthogonal Decomposition Neural Operators
por: Cheng, Zilan, et al.
Publicado: (2025)

A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
por: Chen, Gaohang, et al.
Publicado: (2025)

SVD-Preconditioned Gradient Descent Method for Solving Nonlinear Least Squares Problems
por: Chang, Zhipeng, et al.
Publicado: (2026)

Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure
por: Geng, Yuwei, et al.
Publicado: (2024)