Copertina

Point Source Identification Using Singularity Enriched Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hu, Tianhao, Jin, Bangti, Zhou, Zhi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Solving Poisson Problems in Polygonal Domains with Singularity Enriched Physics Informed Neural Networks
di: Hu, Tianhao, et al.
Pubblicazione: (2023)

An Iterative Deep Ritz Method for Monotone Elliptic Problems
di: Hu, Tianhao, et al.
Pubblicazione: (2025)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
di: Jin, Bangti, et al.
Pubblicazione: (2025)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
di: Dai, Yongcheng, et al.
Pubblicazione: (2023)

Conditional Stability and Numerical Reconstruction of a Parabolic Inverse Source Problem Using Carleman Estimates
di: Hu, Tianhao, et al.
Pubblicazione: (2025)

Point Source Identification in Subdiffusion from A Posteriori Internal Measurement
di: Huang, Kuang, et al.
Pubblicazione: (2024)

Numerical Analysis of Space-Time Dependent Source Identification in Subdiffusion Equations
di: Cen, Siyu, et al.
Pubblicazione: (2026)

Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks
di: Cen, Siyu, et al.
Pubblicazione: (2024)

Early Stopping of Untrained Convolutional Neural Networks
di: Jahn, Tim, et al.
Pubblicazione: (2024)

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

N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
di: Baek, Jonghyuk, et al.
Pubblicazione: (2024)

Radial Müntz-Szász Networks: Neural Architectures with Learnable Power Bases for Multidimensional Singularities
di: N'guessan, Gnankan Landry Regis, et al.
Pubblicazione: (2026)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
di: Gao, Yihang, et al.
Pubblicazione: (2022)

Solving Inverse Acoustic Obstacle Scattering Problem with Phaseless Far-Field Measurement Using Deep Neural Network Surrogates
di: Fan, Yuxin, et al.
Pubblicazione: (2025)

Simultaneous Identification of Coefficients and Source in a Subdiffusion Equation from One Passive Measurement
di: Deng, Maolin, et al.
Pubblicazione: (2025)

Component Fourier Neural Operator for Singularly Perturbed Differential Equations
di: Li, Ye, et al.
Pubblicazione: (2024)

Numerical Approximation and Analysis of the Inverse Robin Problem Using the Kohn-Vogelius Method
di: Burman, Erik, et al.
Pubblicazione: (2025)

Optimizing Coarse Propagators in Parareal Algorithms
di: Jin, Bangti, et al.
Pubblicazione: (2023)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
di: Wang, Sen, et al.
Pubblicazione: (2024)

Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
di: Vaidyan, Kevin Kurian Thomas, et al.
Pubblicazione: (2026)

Dual-Balancing for Physics-Informed Neural Networks
di: Zhou, Chenhong, et al.
Pubblicazione: (2025)

Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
di: Mattey, Revanth, et al.
Pubblicazione: (2024)

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
di: Zhou, Hanfei, et al.
Pubblicazione: (2025)

Exponential Expressivity of ReLU$^k$ Neural Networks on Gevrey Classes with Point Singularities
di: Opschoor, Joost A. A., et al.
Pubblicazione: (2024)

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

Numerical Recovery of the Diffusion Coefficient in Diffusion Equations from Terminal Measurement
di: Jin, Bangti, et al.
Pubblicazione: (2024)

Regularity Analysis and High-Order Time Stepping Scheme for Quasilinear Subdiffusion
di: Jin, Bangti, et al.
Pubblicazione: (2024)

Dual Variational Neural Network for the $p$-Laplace Problem
di: Hu, Tianhao, et al.
Pubblicazione: (2026)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
di: Zhou, Wei, et al.
Pubblicazione: (2024)

Stochastic Rounding Increases Small Singular Values
di: Ma, Linkai, et al.
Pubblicazione: (2026)

Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
di: Chen, Chuqi, et al.
Pubblicazione: (2024)

Multigrade Neural Network Approximation
di: Zhang, Shijun, et al.
Pubblicazione: (2026)

Learn Singularly Perturbed Solutions via Homotopy Dynamics
di: Chen, Chuqi, et al.
Pubblicazione: (2025)

In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
di: Cole, Frank, et al.
Pubblicazione: (2024)

Preconditioning for Physics-Informed Neural Networks
di: Liu, Songming, et al.
Pubblicazione: (2024)

THINNs: Thermodynamically Informed Neural Networks
di: Castro, Javier, et al.
Pubblicazione: (2025)

Learning Singularity-Encoded Green's Functions with Application to Iterative Methods
di: Sun, Qi, et al.
Pubblicazione: (2025)

Better Neural PDE Solvers Through Data-Free Mesh Movers
di: Hu, Peiyan, et al.
Pubblicazione: (2023)

A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition
di: Wesel, Frederiek, et al.
Pubblicazione: (2024)

Estimating condition number with Graph Neural Networks
di: Carson, Erin, et al.
Pubblicazione: (2026)