Saved in:
| Main Authors: | Wang, Sen, Zhao, Peizhi, Song, Tao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.13185 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)
by: Wang, Sen, et al.
Published: (2024)
Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)
by: Ma, Qinglong, et al.
Published: (2024)
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?
by: Zhou, Zijian, et al.
Published: (2024)
by: Zhou, Zijian, et al.
Published: (2024)
Non-perturbative asymptotics of the eigenvalues of the spheroidal equation
by: Meynig, Max
Published: (2025)
by: Meynig, Max
Published: (2025)
Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)
by: Chen, Tianyi, et al.
Published: (2025)
Sharp spectral stability for a class of singularly perturbed pseudo-differential operators
by: Cornean, Horia D., et al.
Published: (2023)
by: Cornean, Horia D., et al.
Published: (2023)
Local neural operator for solving transient partial differential equations on varied domains
by: Li, Hongyu, et al.
Published: (2022)
by: Li, Hongyu, et al.
Published: (2022)
The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models
by: Wentland, Christopher R., et al.
Published: (2024)
by: Wentland, Christopher R., et al.
Published: (2024)
Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning
by: Song, Jin, et al.
Published: (2024)
by: Song, Jin, et al.
Published: (2024)
Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)
by: Li, Xin, et al.
Published: (2025)
Learning Heat-based Equations in Self-similar variables
by: Wang, Shihao, et al.
Published: (2026)
by: Wang, Shihao, et al.
Published: (2026)
A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)
by: Zhao, Yingxue, et al.
Published: (2024)
Stochastic solutions and singular partial differential equations
by: Mendes, R. Vilela
Published: (2022)
by: Mendes, R. Vilela
Published: (2022)
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
by: Zhang, Handi, et al.
Published: (2024)
by: Zhang, Handi, et al.
Published: (2024)
PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations
by: Zhu, Min, et al.
Published: (2025)
by: Zhu, Min, et al.
Published: (2025)
Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)
by: Wang, Zhiwei, et al.
Published: (2024)
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
by: Zhou, Shaoqian, et al.
Published: (2025)
by: Zhou, Shaoqian, et al.
Published: (2025)
AI paradigm for solving differential equations: first-principles data generation and scale-dilation operator AI solver
by: Gong, Xiangshu, et al.
Published: (2025)
by: Gong, Xiangshu, et al.
Published: (2025)
Asymptotic behavior of eigenvalues of large rank perturbations of large random matrices
by: Afanasiev, Ievgenii, et al.
Published: (2025)
by: Afanasiev, Ievgenii, et al.
Published: (2025)
Approximate Lie symmetries and singular perturbation theory
by: Dear, Alexander J., et al.
Published: (2023)
by: Dear, Alexander J., et al.
Published: (2023)
The investigation of singular integro-differential equations relating to adhesive contact problems of the theory of viscoelasticity
by: Shavlakadze, Nugzar, et al.
Published: (2024)
by: Shavlakadze, Nugzar, et al.
Published: (2024)
Learning by solving differential equations
by: Dherin, Benoit, et al.
Published: (2025)
by: Dherin, Benoit, et al.
Published: (2025)
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
by: Ma, Genwei, et al.
Published: (2026)
by: Ma, Genwei, et al.
Published: (2026)
Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)
by: Zhang, Jingdong, et al.
Published: (2024)
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
by: Orozco, Rafael, et al.
Published: (2024)
by: Orozco, Rafael, et al.
Published: (2024)
Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)
by: Meng, Bin, et al.
Published: (2024)
Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)
by: Liu, Ziyang, et al.
Published: (2024)
A DNN Biophysics Model with Topological and Electrostatic Features
by: Sliheet, Elyssa, et al.
Published: (2024)
by: Sliheet, Elyssa, et al.
Published: (2024)
Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)
by: Basterrech, Sebastian, et al.
Published: (2025)
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)
by: Menon, Sidharth S., et al.
Published: (2026)
Physics-Informed Deep Learning of Rate-and-State Fault Friction
by: Rucker, Cody, et al.
Published: (2023)
by: Rucker, Cody, et al.
Published: (2023)
Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
by: Mishra, Aashwin, et al.
Published: (2026)
by: Mishra, Aashwin, et al.
Published: (2026)
Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)
by: Eiras, Francisco, et al.
Published: (2023)
Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)
by: Liu, Chengyu, et al.
Published: (2026)
Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)
by: Scholl, Philipp, et al.
Published: (2022)
A Learning-based Domain Decomposition Method
by: Wu, Rui, et al.
Published: (2025)
by: Wu, Rui, et al.
Published: (2025)
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
by: Behnoudfar, Pouria, et al.
Published: (2025)
by: Behnoudfar, Pouria, et al.
Published: (2025)
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)
by: Zang, Yaohua, et al.
Published: (2025)
PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)
by: Wu, Junyi, et al.
Published: (2025)
Similar Items
-
General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024) -
Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025) -
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024) -
Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?
by: Zhou, Zijian, et al.
Published: (2024) -
Non-perturbative asymptotics of the eigenvalues of the spheroidal equation
by: Meynig, Max
Published: (2025)