Preconditioning for Physics-Informed Neural Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Liu, Songming, Su, Chang, Yao, Jiachen, Hao, Zhongkai, Su, Hang, Wu, Youjia, Zhu, Jun |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
por: Hao, Zhongkai, et al.
Publicado: (2024)
por: Hao, Zhongkai, et al.
Publicado: (2024)
Dual-Balancing for Physics-Informed Neural Networks
por: Zhou, Chenhong, et al.
Publicado: (2025)
por: Zhou, Chenhong, et al.
Publicado: (2025)
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
por: Jin, Bangti, et al.
Publicado: (2025)
por: Jin, Bangti, et al.
Publicado: (2025)
Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
por: Alegría, Samuel A. Cruz, et al.
Publicado: (2025)
por: Alegría, Samuel A. Cruz, et al.
Publicado: (2025)
E-PINNs: Epistemic Physics-Informed Neural Networks
por: Jacob, Bruno, et al.
Publicado: (2025)
por: Jacob, Bruno, et al.
Publicado: (2025)
Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
por: Guan, Jiajing, et al.
Publicado: (2024)
por: Guan, Jiajing, et al.
Publicado: (2024)
Bayesian Physics Informed Neural Networks for Linear Inverse problems
por: Mohammad-Djafari, Ali
Publicado: (2025)
por: Mohammad-Djafari, Ali
Publicado: (2025)
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
por: Matsubara, Takashi, et al.
Publicado: (2023)
por: Matsubara, Takashi, et al.
Publicado: (2023)
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
por: Park, Youngjae, et al.
Publicado: (2026)
por: Park, Youngjae, et al.
Publicado: (2026)
Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
por: Cheng, Xiaoran, et al.
Publicado: (2024)
por: Cheng, Xiaoran, et al.
Publicado: (2024)
Discovering Scaling Exponents with Physics-Informed Müntz-Szász Networks
por: N'guessan, Gnankan Landry Regis, et al.
Publicado: (2026)
por: N'guessan, Gnankan Landry Regis, et al.
Publicado: (2026)
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
por: Fang, Zhiwei, et al.
Publicado: (2023)
por: Fang, Zhiwei, et al.
Publicado: (2023)
eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
por: Lotfalian, Amin, et al.
Publicado: (2025)
por: Lotfalian, Amin, et al.
Publicado: (2025)
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
por: Yu, Yifan, et al.
Publicado: (2025)
por: Yu, Yifan, et al.
Publicado: (2025)
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
por: Yang, Jianan, et al.
Publicado: (2026)
por: Yang, Jianan, et al.
Publicado: (2026)
THINNs: Thermodynamically Informed Neural Networks
por: Castro, Javier, et al.
Publicado: (2025)
por: Castro, Javier, et al.
Publicado: (2025)
WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
por: Zhu, Bokai, et al.
Publicado: (2026)
por: Zhu, Bokai, et al.
Publicado: (2026)
Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
por: Zhou, Hanfei, et al.
Publicado: (2025)
por: Zhou, Hanfei, et al.
Publicado: (2025)
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
por: Sahin, T., et al.
Publicado: (2023)
por: Sahin, T., et al.
Publicado: (2023)
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
por: Andre-Sloan, Sebastien, et al.
Publicado: (2026)
por: Andre-Sloan, Sebastien, et al.
Publicado: (2026)
Physics-Informed Geometry-Aware Neural Operator
por: Zhong, Weiheng, et al.
Publicado: (2024)
por: Zhong, Weiheng, et al.
Publicado: (2024)
Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
por: Hao, Wenrui, et al.
Publicado: (2024)
por: Hao, Wenrui, et al.
Publicado: (2024)
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
por: Zhou, Wei, et al.
Publicado: (2024)
por: Zhou, Wei, et al.
Publicado: (2024)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
por: Gao, Yihang, et al.
Publicado: (2022)
por: Gao, Yihang, et al.
Publicado: (2022)
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
por: Kalyan, Anirudh, et al.
Publicado: (2025)
por: Kalyan, Anirudh, et al.
Publicado: (2025)
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
por: Mustajab, Abdul Hannan, et al.
Publicado: (2024)
por: Mustajab, Abdul Hannan, et al.
Publicado: (2024)
Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
por: Rao, Xiang, et al.
Publicado: (2025)
por: Rao, Xiang, et al.
Publicado: (2025)
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
por: Wang, Hong, et al.
Publicado: (2024)
por: Wang, Hong, et al.
Publicado: (2024)
SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
por: Su, Hua, et al.
Publicado: (2025)
por: Su, Hua, et al.
Publicado: (2025)
Preconditioned FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
por: Griese, Franziska, et al.
Publicado: (2024)
por: Griese, Franziska, et al.
Publicado: (2024)
Preconditioning for Accelerated Gradient Descent Optimization and Regularization
por: Ye, Qiang
Publicado: (2024)
por: Ye, Qiang
Publicado: (2024)
Preconditioned Additive Gaussian Processes with Fourier Acceleration
por: Wagner, Theresa, et al.
Publicado: (2025)
por: Wagner, Theresa, et al.
Publicado: (2025)
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Physics-embedded Fourier Neural Network for Partial Differential Equations
por: Xu, Qingsong, et al.
Publicado: (2024)
por: Xu, Qingsong, et al.
Publicado: (2024)
Learning from Integral Losses in Physics Informed Neural Networks
por: Saleh, Ehsan, et al.
Publicado: (2023)
por: Saleh, Ehsan, et al.
Publicado: (2023)
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
por: Lei, Guanhang, et al.
Publicado: (2023)
por: Lei, Guanhang, et al.
Publicado: (2023)
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
por: Jacob, Bruno, et al.
Publicado: (2024)
por: Jacob, Bruno, et al.
Publicado: (2024)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
por: Gonon, Lukas, et al.
Publicado: (2024)
por: Gonon, Lukas, et al.
Publicado: (2024)
Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
por: Gao, Zhiwei, et al.
Publicado: (2025)
por: Gao, Zhiwei, et al.
Publicado: (2025)
Ejemplares similares
-
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
por: Hao, Zhongkai, et al.
Publicado: (2024) -
Dual-Balancing for Physics-Informed Neural Networks
por: Zhou, Chenhong, et al.
Publicado: (2025) -
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
por: Jin, Bangti, et al.
Publicado: (2025) -
Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
por: Alegría, Samuel A. Cruz, et al.
Publicado: (2025) -
E-PINNs: Epistemic Physics-Informed Neural Networks
por: Jacob, Bruno, et al.
Publicado: (2025)