Cover Image

DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Zijiang, Qiu, Zhongwei, Fu, Dongmei
Format:	Preprint
Published:	2022
Subjects:	Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
by: Zhang, Tao, et al.
Published: (2026)

Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
by: Yang, Yu, et al.
Published: (2023)

On the Stability and Convergence of Physics Informed Neural Networks
by: Gazoulis, Dimitrios, et al.
Published: (2023)

Positivity-Preserving and Entropy-Stable Oscillation-Eliminating DGSEM for the Compressible Euler Equations on Curvilinear Meshes with Adaptive Mesh Refinement
by: Yang, Jieling, et al.
Published: (2026)

Coupling Physics Informed Neural Networks with External Solvers
by: Halder, Rahul, et al.
Published: (2025)

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

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)

Physics Informed Neural Networks for heat conduction with phase change
by: Madir, Bahae-Eddine, et al.
Published: (2024)

Predictive Moving Sample Method for Physics-Informed Neural Solvers of Time-Dependent PDEs
by: Xu, Beining, et al.
Published: (2026)

Physics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems
by: Chenaud, Marien, et al.
Published: (2024)

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

A Physics-Informed Neural Network approach for compartmental epidemiological models
by: Millevoi, Caterina, et al.
Published: (2023)

Optimizing Variational Physics-Informed Neural Networks Using Least Squares
by: Uriarte, Carlos, et al.
Published: (2024)

PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
by: Stiasny, Jochen, et al.
Published: (2023)

The Ill-Posed Foundations of Physics-Informed Neural Networks and Their Finite-Difference Variants
by: Langer, Andreas
Published: (2026)

Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality
by: Zhou, Qijia, et al.
Published: (2026)

Extended Interface Physics-Informed Neural Networks Method for Moving Interface Problems
by: Bi, Ran, et al.
Published: (2025)

Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)

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

Runge-Kutta Physics Informed Neural Networks: Formulation and Analysis
by: Akrivis, Georgios, et al.
Published: (2024)

From Mesh to Neural Nets: A Multi-Method Evaluation of Physics-Informed Neural Networks and Galerkin Finite Element Method for Solving Nonlinear Convection-Reaction-Diffusion Equations
by: Hasan, Fardous, et al.
Published: (2024)

Beyond Free-Stream Preservation: Transport Polynomial Exactness for Moving-Mesh Methods under Arbitrary Mesh Motion
by: Cai, Chaoyi, et al.
Published: (2026)

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models
by: Difonzo, Fabio V., et al.
Published: (2025)

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

Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
by: Li, Guojie, et al.
Published: (2026)

AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2025)

Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
by: Mishra, Rishi, et al.
Published: (2025)

Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)

E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)

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)

PhysicsSolver: Transformer-Enhanced Physics-Informed Neural Networks for Forward and Forecasting Problems in Partial Differential Equations
by: Zhu, Zhenyi, et al.
Published: (2025)

$ε$-rank and the Staircase Phenomenon: New Insights into Neural Network Training Dynamics
by: Yang, Jiang, et al.
Published: (2024)

Solution of Advection Equation with Discontinuous Initial and Boundary Conditions via Physics-Informed Neural Networks
by: Khosravi, Omid, et al.
Published: (2026)

Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology
by: Sierpe, Moises, et al.
Published: (2025)

Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
by: Nohra, Michel, et al.
Published: (2024)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025)

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)

Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
by: Matsubara, Takashi, et al.
Published: (2023)

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

Energy-Equidistributed Moving Sampling Physics-informed Neural Networks for Solving Conservative Partial Differential Equations
by: Gao, Qinjiao, et al.
Published: (2025)