Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Jin, Bangti, Wu, Longjun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Point Source Identification Using Singularity Enriched Neural Networks
by: Hu, Tianhao, et al.
Published: (2024)
by: Hu, Tianhao, et al.
Published: (2024)
An Iterative Deep Ritz Method for Monotone Elliptic Problems
by: Hu, Tianhao, et al.
Published: (2025)
by: Hu, Tianhao, et al.
Published: (2025)
Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023)
by: Dai, Yongcheng, et al.
Published: (2023)
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023)
by: Fang, Zhiwei, et al.
Published: (2023)
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)
by: Liu, Songming, et al.
Published: (2024)
eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
by: Lotfalian, Amin, et al.
Published: (2025)
by: Lotfalian, Amin, et al.
Published: (2025)
Solving Poisson Problems in Polygonal Domains with Singularity Enriched Physics Informed Neural Networks
by: Hu, Tianhao, et al.
Published: (2023)
by: Hu, Tianhao, et al.
Published: (2023)
Stochastic Convergence Analysis of Inverse Potential Problem
by: Jin, Bangti, et al.
Published: (2024)
by: Jin, Bangti, et al.
Published: (2024)
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)
by: Hwang, Youngsik, et al.
Published: (2024)
Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)
by: Zhou, Chenhong, et al.
Published: (2025)
E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)
by: Jacob, Bruno, et al.
Published: (2025)
Stochastic Gradient Descent for Nonlinear Inverse Problems in Banach Spaces
by: Jin, Bangti, et al.
Published: (2026)
by: Jin, Bangti, et al.
Published: (2026)
Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025)
by: Mohammad-Djafari, Ali
Published: (2025)
Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)
by: Guan, Jiajing, et al.
Published: (2024)
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
by: Matsubara, Takashi, et al.
Published: (2023)
by: Matsubara, Takashi, et al.
Published: (2023)
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)
by: Xia, Lu, et al.
Published: (2023)
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
by: Shin, Jeesuk, et al.
Published: (2026)
by: Shin, Jeesuk, et al.
Published: (2026)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)
by: Gonon, Lukas, et al.
Published: (2024)
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
by: Liu, Chaoyu, et al.
Published: (2023)
by: Liu, Chaoyu, et al.
Published: (2023)
Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
by: Cheng, Xiaoran, et al.
Published: (2024)
by: Cheng, Xiaoran, et al.
Published: (2024)
Early Stopping of Untrained Convolutional Neural Networks
by: Jahn, Tim, et al.
Published: (2024)
by: Jahn, Tim, et al.
Published: (2024)
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
by: Yu, Yifan, et al.
Published: (2025)
by: Yu, Yifan, et al.
Published: (2025)
THINNs: Thermodynamically Informed Neural Networks
by: Castro, Javier, et al.
Published: (2025)
by: Castro, Javier, et al.
Published: (2025)
Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
by: Qian, Yianxia, et al.
Published: (2024)
by: Qian, Yianxia, et al.
Published: (2024)
Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
by: Zhou, Hanfei, et al.
Published: (2025)
by: Zhou, Hanfei, et al.
Published: (2025)
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)
by: Sahin, T., et al.
Published: (2023)
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
by: Zhang, Borong, et al.
Published: (2022)
by: Zhang, Borong, et al.
Published: (2022)
Physics-Informed Geometry-Aware Neural Operator
by: Zhong, Weiheng, et al.
Published: (2024)
by: Zhong, Weiheng, et al.
Published: (2024)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)
by: Katende, Ronald
Published: (2025)
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)
by: Zhou, Wei, et al.
Published: (2024)
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)
by: Gao, Yihang, et al.
Published: (2022)
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
by: Kalyan, Anirudh, et al.
Published: (2025)
by: Kalyan, Anirudh, et al.
Published: (2025)
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
by: Yang, Jianan, et al.
Published: (2026)
by: Yang, Jianan, et al.
Published: (2026)
Streaming Krylov-Accelerated Stochastic Gradient Descent
by: Thomas, Stephen
Published: (2025)
by: Thomas, Stephen
Published: (2025)
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
by: Chen, Chuqi, et al.
Published: (2024)
by: Chen, Chuqi, et al.
Published: (2024)
Neural Network Approach to Stochastic Dynamics for Smooth Multimodal Density Estimation
by: Zarezadeh, Z., et al.
Published: (2025)
by: Zarezadeh, Z., et al.
Published: (2025)
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
by: Katende, Ronald
Published: (2025)
by: Katende, Ronald
Published: (2025)
Learning from Integral Losses in Physics Informed Neural Networks
by: Saleh, Ehsan, et al.
Published: (2023)
by: Saleh, Ehsan, et al.
Published: (2023)
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)
by: Lei, Guanhang, et al.
Published: (2023)
Similar Items
-
Point Source Identification Using Singularity Enriched Neural Networks
by: Hu, Tianhao, et al.
Published: (2024) -
An Iterative Deep Ritz Method for Monotone Elliptic Problems
by: Hu, Tianhao, et al.
Published: (2025) -
Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023) -
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023) -
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)