Neural Networks are Integrable
Fuente:
arXiv
Saved in:
| Main Author: | Liu, Yucong |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Energetic Variational Neural Network Discretizations of Gradient Flows
by: Hu, Ziqing, et al.
Published: (2022)
by: Hu, Ziqing, et al.
Published: (2022)
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions
by: Zeng, Weiheng, et al.
Published: (2025)
by: Zeng, Weiheng, et al.
Published: (2025)
Least-Squares Neural Network (LSNN) Method for Scalar Hyperbolic Partial Differential Equations
by: Liu, Min, et al.
Published: (2026)
by: Liu, Min, et al.
Published: (2026)
Integrability of weak mixed first-order derivatives and convergence rates of scrambled digital nets
by: Liu, Yang
Published: (2025)
by: Liu, Yang
Published: (2025)
Computational Math with Neural Networks is Hard
by: Feischl, Michael, et al.
Published: (2025)
by: Feischl, Michael, et al.
Published: (2025)
PhysicsSolver: Transformer-Enhanced Physics-Informed Neural Networks for Forward and Forecasting Problems in Partial Differential Equations
by: Zhu, Zhenyi, et al.
Published: (2025)
by: Zhu, Zhenyi, et al.
Published: (2025)
On the Stability and Convergence of Physics Informed Neural Networks
by: Gazoulis, Dimitrios, et al.
Published: (2023)
by: Gazoulis, Dimitrios, et al.
Published: (2023)
Can Neural Networks learn Finite Elements?
by: Novo, Julia, et al.
Published: (2024)
by: Novo, Julia, et al.
Published: (2024)
Adaptive Finite Element Interpolated Neural Networks
by: Badia, Santiago, et al.
Published: (2024)
by: Badia, Santiago, et al.
Published: (2024)
Neural Networks in Numerical Analysis and Approximation Theory
by: Romera, Gonzalo
Published: (2024)
by: Romera, Gonzalo
Published: (2024)
Early Stopping of Untrained Convolutional Neural Networks
by: Jahn, Tim, et al.
Published: (2024)
by: Jahn, Tim, et al.
Published: (2024)
Domain Decomposition Subspace Neural Network Method for Solving Linear and Nonlinear Partial Differential Equations
by: Fu, Zhenxing, et al.
Published: (2025)
by: Fu, Zhenxing, et al.
Published: (2025)
From Obstacle Problems to Neural Insights: Feed Forward Neural Network Modeling of Ice Thickness
by: Chawla, Kapil, et al.
Published: (2024)
by: Chawla, Kapil, et al.
Published: (2024)
Learning Hamiltonian Systems with Pseudo-symplectic Neural Network
by: Cheng, Xupeng, et al.
Published: (2025)
by: Cheng, Xupeng, et al.
Published: (2025)
The Neural Network Approach to Inverse Problems in Differential Equations
by: Xu, Kailai, et al.
Published: (2019)
by: Xu, Kailai, et al.
Published: (2019)
Coupling Physics Informed Neural Networks with External Solvers
by: Halder, Rahul, et al.
Published: (2025)
by: Halder, Rahul, et al.
Published: (2025)
Greedy Algorithm for Neural Networks for Indefinite Elliptic Problems
by: Hong, Qingguo, et al.
Published: (2024)
by: Hong, Qingguo, et al.
Published: (2024)
Dual Variational Neural Network for the $p$-Laplace Problem
by: Hu, Tianhao, et al.
Published: (2026)
by: Hu, Tianhao, et al.
Published: (2026)
Stochastic Quadrature Rules for Solving PDEs using Neural Networks
by: Taylor, Jamie M., et al.
Published: (2025)
by: Taylor, Jamie M., et al.
Published: (2025)
RUNNs: Ritz-Uzawa Neural Networks for Solving Variational Problems
by: Herrera, Pablo, et al.
Published: (2026)
by: Herrera, Pablo, et al.
Published: (2026)
Accelerating Algebraic Multigrid Methods via Artificial Neural Networks
by: Antonietti, Paola F., et al.
Published: (2021)
by: Antonietti, Paola F., et al.
Published: (2021)
Overlapping Schwarz Preconditioners for Randomized Neural Networks with Domain Decomposition
by: Shang, Yong, et al.
Published: (2024)
by: Shang, Yong, et al.
Published: (2024)
Solving Partial Differential Equations Using Artificial Neural Networks
by: Uriarte, Carlos
Published: (2024)
by: Uriarte, Carlos
Published: (2024)
Solving High-Dimensional PDEs Using Linearized Neural Networks
by: Mao, Tong, et al.
Published: (2026)
by: Mao, Tong, et al.
Published: (2026)
Physics Informed Neural Networks for heat conduction with phase change
by: Madir, Bahae-Eddine, et al.
Published: (2024)
by: Madir, Bahae-Eddine, et al.
Published: (2024)
A Kolmogorov High Order Deep Neural Network for High Frequency Partial Differential Equations in High Dimensions
by: Zhang, Yaqin, et al.
Published: (2025)
by: Zhang, Yaqin, et al.
Published: (2025)
A Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) Framework for Non-linear Finite Element Analysis
by: Liu, Yingjian, et al.
Published: (2025)
by: Liu, Yingjian, et al.
Published: (2025)
Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)
by: Liu, Li, et al.
Published: (2022)
A Physics-Informed Neural Network approach for compartmental epidemiological models
by: Millevoi, Caterina, et al.
Published: (2023)
by: Millevoi, Caterina, et al.
Published: (2023)
Neural Network Acceleration of Iterative Methods for Nonlinear Schrödinger Eigenvalue Problems
by: Peterseim, Daniel, et al.
Published: (2025)
by: Peterseim, Daniel, et al.
Published: (2025)
Rank Inspired Neural Network for solving linear partial differential equations
by: Peng, Wentao, et al.
Published: (2025)
by: Peng, Wentao, et al.
Published: (2025)
Hybrid PDE-Deep Neural Network Model for Calcium Dynamics in Neurons
by: Gurung, Abel, et al.
Published: (2024)
by: Gurung, Abel, et al.
Published: (2024)
Adaptive-Growth Randomized Neural Networks for PDEs: Algorithms and Numerical Analysis
by: Dang, Haoning, et al.
Published: (2024)
by: Dang, Haoning, et al.
Published: (2024)
Optimizing Variational Physics-Informed Neural Networks Using Least Squares
by: Uriarte, Carlos, et al.
Published: (2024)
by: Uriarte, Carlos, et al.
Published: (2024)
Generalized Transferable Neural Networks for Steady-State Partial Differential Equations
by: Cheng, Tao, et al.
Published: (2026)
by: Cheng, Tao, et al.
Published: (2026)
Orthogonal Constrained Neural Networks for Solving Structured Inverse Eigenvalue Problems
by: Zhang, Shuai, et al.
Published: (2024)
by: Zhang, Shuai, et al.
Published: (2024)
A Unified Benchmark of Physics-Informed Neural Networks and Kolmogorov-Arnold Networks for Ordinary and Partial Differential Equations
by: Dzimah, Salvador K., et al.
Published: (2026)
by: Dzimah, Salvador K., et al.
Published: (2026)
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)
by: Liu, Songming, et al.
Published: (2024)
Deep ReLU Neural Network Emulation in High-Frequency Acoustic Scattering
by: Henríquez, Fernando, et al.
Published: (2024)
by: Henríquez, Fernando, et al.
Published: (2024)
The Ill-Posed Foundations of Physics-Informed Neural Networks and Their Finite-Difference Variants
by: Langer, Andreas
Published: (2026)
by: Langer, Andreas
Published: (2026)
Similar Items
-
Energetic Variational Neural Network Discretizations of Gradient Flows
by: Hu, Ziqing, et al.
Published: (2022) -
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions
by: Zeng, Weiheng, et al.
Published: (2025) -
Least-Squares Neural Network (LSNN) Method for Scalar Hyperbolic Partial Differential Equations
by: Liu, Min, et al.
Published: (2026) -
Integrability of weak mixed first-order derivatives and convergence rates of scrambled digital nets
by: Liu, Yang
Published: (2025) -
Computational Math with Neural Networks is Hard
by: Feischl, Michael, et al.
Published: (2025)