Copertina

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Arisaka, Sohei, Li, Qianxiao
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators
di: Herb, Julius, et al.
Pubblicazione: (2025)

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
di: Bioli, Ivan, et al.
Pubblicazione: (2025)

Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers
di: Yao, Dingling, et al.
Pubblicazione: (2025)

Parallel-in-Time Probabilistic Numerical ODE Solvers
di: Bosch, Nathanael, et al.
Pubblicazione: (2023)

DynGMA: a robust approach for learning stochastic differential equations from data
di: Zhu, Aiqing, et al.
Pubblicazione: (2024)

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

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

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

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

Preconditioning for Accelerated Gradient Descent Optimization and Regularization
di: Ye, Qiang
Pubblicazione: (2024)

Streaming Krylov-Accelerated Stochastic Gradient Descent
di: Thomas, Stephen
Pubblicazione: (2025)

Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems
di: Victor, Viny Saajan, et al.
Pubblicazione: (2024)

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

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

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
di: Liu, Ye, et al.
Pubblicazione: (2024)

Learning by solving differential equations
di: Dherin, Benoit, et al.
Pubblicazione: (2025)

SciML Agents: Write the Solver, Not the Solution
di: Gaonkar, Saarth, et al.
Pubblicazione: (2025)

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

Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
di: Sun, Shuwen, et al.
Pubblicazione: (2024)

Transolver: A Fast Transformer Solver for PDEs on General Geometries
di: Wu, Haixu, et al.
Pubblicazione: (2024)

Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements
di: Krämer, Nicholas
Pubblicazione: (2024)

Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
di: Hegde, Disha, et al.
Pubblicazione: (2026)

A randomized algorithm to solve reduced rank operator regression
di: Turri, Giacomo, et al.
Pubblicazione: (2023)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
di: Pförtner, Marvin, et al.
Pubblicazione: (2022)

Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
di: Lee, Youngkyu, et al.
Pubblicazione: (2025)

Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
di: Yang, Liu, et al.
Pubblicazione: (2023)

A real-time battle situation intelligent awareness system based on Meta-learning & RNN
di: Li, Yuchun, et al.
Pubblicazione: (2025)

Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space
di: Yue, Xihang, et al.
Pubblicazione: (2024)

PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
di: Cheng, Chun-Wun, et al.
Pubblicazione: (2025)

Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
di: Baba, Zakaria, et al.
Pubblicazione: (2026)

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
di: Weiland, Tim, et al.
Pubblicazione: (2025)

Deep Neural networks for solving high-dimensional parabolic partial differential equations
di: Zhang, Wenzhong, et al.
Pubblicazione: (2026)

A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
di: Storm, J., et al.
Pubblicazione: (2024)

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
di: Sun, Yuxin, et al.
Pubblicazione: (2022)

Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
di: Wang, Yizheng, et al.
Pubblicazione: (2024)

Learning Neural PDE Solvers with Convergence Guarantees
di: Hsieh, Jun-Ting, et al.
Pubblicazione: (2019)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
di: Zhang, Huan, et al.
Pubblicazione: (2024)

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
di: Zeudong, Etienne, et al.
Pubblicazione: (2025)

A shallow physics-informed neural network for solving partial differential equations on surfaces
di: Hu, Wei-Fan, et al.
Pubblicazione: (2022)

Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
di: Tyurin, Alexander
Pubblicazione: (2025)