Cover Image

Adaptive-Growth Randomized Neural Networks for PDEs: Algorithms and Numerical Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dang, Haoning, Wang, Fei, Jiang, Song
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive-Growth Randomized Neural Networks for Level-Set Computation of Multivalued Nonlinear First-Order PDEs with Hyperbolic Characteristics
by: Dang, Haoning, et al.
Published: (2026)

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
by: Dang, Haoning, et al.
Published: (2026)

Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
by: Yang, You, et al.
Published: (2026)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
by: Jiang, Zhaoxi, et al.
Published: (2025)

An Adaptive CUR Algorithm and its Application to Reduced-Order Modeling of Random PDEs
by: Palkar, Grishma, et al.
Published: (2025)

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs
by: De Ryck, T., et al.
Published: (2025)

Adaptive Randomized Neural Networks with Locally Activation Function: Theory and Algorithm for Solving PDEs
by: Bi, Ran, et al.
Published: (2026)

Numerical Analysis of Unsupervised Learning Approaches for Parameter Identification in PDEs
by: Cen, Siyu, et al.
Published: (2025)

Neural Networks in Numerical Analysis and Approximation Theory
by: Romera, Gonzalo
Published: (2024)

Overlapping Schwarz Preconditioners for Randomized Neural Networks with Domain Decomposition
by: Shang, Yong, et al.
Published: (2024)

Neural Measures for learning distributions of Random PDEs
by: Arampatzis, Georgios, et al.
Published: (2025)

Local Randomized Neural Networks with Discontinuous Galerkin Methods for KdV-type and Burgers Equations
by: Sun, Jingbo, et al.
Published: (2024)

Recent Advances in Numerical Solutions for Hamilton-Jacobi PDEs
by: Meng, Tingwei, et al.
Published: (2025)

Stochastic Quadrature Rules for Solving PDEs using Neural Networks
by: Taylor, Jamie M., et al.
Published: (2025)

Solving High-Dimensional PDEs Using Linearized Neural Networks
by: Mao, Tong, et al.
Published: (2026)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Numerical Analysis of Stochastic Elliptic Variational Inequalities of the First Kind
by: Zhu, Chenhui, et al.
Published: (2026)

PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces
by: Jain, Pranav, et al.
Published: (2026)

New High-Order Numerical Methods for Hyperbolic Systems of Nonlinear PDEs with Uncertainties
by: Chertock, Alina, et al.
Published: (2023)

Numerical Analysis of Stabilization for Random Hyperbolic Systems of Conservation Laws
by: Chu, Shaoshuai, et al.
Published: (2025)

What Can One Expect When Solving PDEs Using Shallow Neural Networks?
by: He, Roy Y., et al.
Published: (2025)

Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries
by: Zeng, Chenyu, et al.
Published: (2025)

Numerical null controllability of parabolic PDEs using Lagrangian methods
by: Fernandez-Cara, Enrique, et al.
Published: (2024)

Preconditioning and Numerical Stability in Neural Network Training for Parametric PDEs
by: Bachmayr, Markus, et al.
Published: (2026)

Leveraging Lie Group Symmetries to Enhance Physics-Informed Neural Networks for the Fundamental Solution of Linear PDEs
by: Jiao, Xiaopei, et al.
Published: (2024)

ALM-PINNs Algorithms for Solving Nonlinear PDEs and Parameter Inversion Problems
by: Tian, Yimeng, et al.
Published: (2024)

Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
by: Dang, Zhenhua, et al.
Published: (2026)

Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations
by: Li, Yunlong, et al.
Published: (2026)

Numerical Analysis of Locally Adaptive Penalty Methods For The Navier-Stokes Equations
by: Fang, Rui
Published: (2024)

Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
by: Škardová, Kateřina, et al.
Published: (2024)

Numerical Methods and Analysis of Computing Quasiperiodic Systems
by: Jiang, Kai, et al.
Published: (2022)

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
by: Dai, Chen-Yang, et al.
Published: (2026)

A Unified Weighted-Loss Physics-Informed Neural Network for Boundary Layer Problems in Singularly Perturbed PDEs
by: Hu, Wei-Fan, et al.
Published: (2026)

Open Source Implementations of Numerical Algorithms for Computing the Complete Elliptic Integral of the First Kind
by: Zhang, Hong-Yan, et al.
Published: (2022)

Smoothed Circulant Embedding with Applications to Multilevel Monte Carlo Methods for PDEs with Random Coefficients
by: Istratuca, Anastasia, et al.
Published: (2023)

Numerical Reconstruction and Analysis of Backward Semilinear Subdiffusion Problems
by: Wu, Xu, et al.
Published: (2025)

Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
by: Wang, Tian, et al.
Published: (2024)

Generalization Error Analysis of Deep Backward Dynamic Programming for Solving Nonlinear PDEs
by: Ouyang, Du, et al.
Published: (2024)

High Accuracy Techniques Based Adaptive Finite Element Methods for Elliptic PDEs
by: Xiao, Jingjing, 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)