PINN-MG: A physics-informed neural network for mesh generation
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Min, Li, Haisheng, Zhang, Haoxuan, Wu, Xiaoqun, Li, Nan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
by: Perera, Roberto, et al.
Published: (2024)
by: Perera, Roberto, et al.
Published: (2024)
Automated machine learning for physics-informed convolutional neural networks
by: Zhou, Wanyun, et al.
Published: (2024)
by: Zhou, Wanyun, et al.
Published: (2024)
Surface profile recovery from electromagnetic field with physics--informed neural networks
by: Chen, Yuxuan, et al.
Published: (2024)
by: Chen, Yuxuan, et al.
Published: (2024)
VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks
by: Song, Jiahao, et al.
Published: (2024)
by: Song, Jiahao, et al.
Published: (2024)
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
by: Naderibeni, M., et al.
Published: (2024)
by: Naderibeni, M., et al.
Published: (2024)
A Dual-Path neural network model to construct the flame nonlinear thermoacoustic response in the time domain
by: Wu, Jiawei, et al.
Published: (2024)
by: Wu, Jiawei, et al.
Published: (2024)
Simulation of parametrized cardiac electrophysiology in three dimensions using physics-informed neural networks
by: Gomez, Roshan Antony, et al.
Published: (2025)
by: Gomez, Roshan Antony, et al.
Published: (2025)
Physics-informed neural networks with curriculum training for poroelastic flow and deformation processes
by: Bekele, Yared W.
Published: (2024)
by: Bekele, Yared W.
Published: (2024)
A physics-augmented neural network framework for finite strain incompressible viscoelasticity
by: Kalina, Karl A., et al.
Published: (2025)
by: Kalina, Karl A., et al.
Published: (2025)
Inverse design of anisotropic microstructures using physics-augmented neural networks
by: Jadoon, Asghar A., et al.
Published: (2024)
by: Jadoon, Asghar A., et al.
Published: (2024)
Physics-informed neural networks for form-finding of unilateral membrane structures
by: Sibille, Luigi, et al.
Published: (2026)
by: Sibille, Luigi, et al.
Published: (2026)
Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks
by: Wu, Wensi, et al.
Published: (2024)
by: Wu, Wensi, et al.
Published: (2024)
A physics-enhanced multi-modal fused neural network for predicting contamination length interval in pipeline
by: Du, Jian, et al.
Published: (2024)
by: Du, Jian, et al.
Published: (2024)
Energy-based physics-informed neural network for frictionless contact problems under large deformation
by: Bai, Jinshuai, et al.
Published: (2024)
by: Bai, Jinshuai, et al.
Published: (2024)
Investigation of PINN Stability and Robustness for the Euler-Bernoulli Beam Problem
by: Homsnit, Thonn, et al.
Published: (2025)
by: Homsnit, Thonn, et al.
Published: (2025)
Physics-informed neural networks for parameter learning of wildfire spreading
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)
Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks
by: Nai, Dingqi, et al.
Published: (2024)
by: Nai, Dingqi, et al.
Published: (2024)
I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)
by: Abueidda, Diab W., et al.
Published: (2023)
by: Abueidda, Diab W., et al.
Published: (2023)
Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
by: Rosenkranz, Max, et al.
Published: (2024)
by: Rosenkranz, Max, et al.
Published: (2024)
Cross-attention-based bipartite graph neural network for coupled nodal and elemental field prediction in large-deformation sheet material forming
by: Zhao, Yingxue, et al.
Published: (2026)
by: Zhao, Yingxue, et al.
Published: (2026)
Convolutional neural network based reduced order modeling for multiscale problems
by: Zhang, Xuhan, et al.
Published: (2024)
by: Zhang, Xuhan, et al.
Published: (2024)
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
by: Chen, Jungang, et al.
Published: (2023)
by: Chen, Jungang, et al.
Published: (2023)
Convergence of physics-informed neural networks modeling time-harmonic wave fields
by: Schoder, Stefan, et al.
Published: (2025)
by: Schoder, Stefan, et al.
Published: (2025)
Fast training of accurate physics-informed neural networks without gradient descent
by: Datar, Chinmay, et al.
Published: (2024)
by: Datar, Chinmay, et al.
Published: (2024)
Multiscale topology optimization of functionally graded lattice structures based on physics-augmented neural network material models
by: Stollberg, Jonathan, et al.
Published: (2024)
by: Stollberg, Jonathan, et al.
Published: (2024)
Multiscale topology optimization of compressible and nearly incompressible anisotropic hyperelastic structures using physics-augmented neural networks
by: Jadoon, Asghar A., et al.
Published: (2026)
by: Jadoon, Asghar A., et al.
Published: (2026)
A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
by: Linden, Lennart, et al.
Published: (2025)
by: Linden, Lennart, et al.
Published: (2025)
FFV-PINN: A Fast Physics-Informed Neural Network with Simplified Finite Volume Discretization and Residual Correction
by: Wei, Chang, et al.
Published: (2026)
by: Wei, Chang, et al.
Published: (2026)
A novel boundary integrated neural networks for in plane fracture mechanics analysis of elastic and piezoelectric materials
by: Zhang, Peijun, et al.
Published: (2025)
by: Zhang, Peijun, et al.
Published: (2025)
Data-driven building energy efficiency prediction using physics-informed neural networks
by: Michalakopoulos, Vasilis, et al.
Published: (2023)
by: Michalakopoulos, Vasilis, et al.
Published: (2023)
Domain decomposition of large neural network surrogate models
by: Gödde, Timm, et al.
Published: (2026)
by: Gödde, Timm, et al.
Published: (2026)
Bridging Computational Fluid Dynamics Algorithm and Physics-Informed Learning: SIMPLE-PINN for Incompressible Navier-Stokes Equations
by: Wei, Chang, et al.
Published: (2026)
by: Wei, Chang, et al.
Published: (2026)
Multigrid on unstructured meshes with regions of low quality cells
by: Chen, Yuxuan, et al.
Published: (2024)
by: Chen, Yuxuan, et al.
Published: (2024)
Real-time design of architectural structures with differentiable mechanics and neural networks
by: Pastrana, Rafael, et al.
Published: (2024)
by: Pastrana, Rafael, et al.
Published: (2024)
GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure
by: Qin, Yigong, et al.
Published: (2024)
by: Qin, Yigong, et al.
Published: (2024)
Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization
by: Hund, Jonas, et al.
Published: (2025)
by: Hund, Jonas, et al.
Published: (2025)
Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams
by: Schommartz, Jasper O., et al.
Published: (2024)
by: Schommartz, Jasper O., et al.
Published: (2024)
Nonlinear electro-elastic finite element analysis with neural network constitutive models
by: Klein, Dominik K., et al.
Published: (2024)
by: Klein, Dominik K., et al.
Published: (2024)
Seismic analysis based on a new interval method with incomplete information
by: Liang, Shizhong, et al.
Published: (2025)
by: Liang, Shizhong, et al.
Published: (2025)
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs
by: Ding, Shan, et al.
Published: (2026)
by: Ding, Shan, et al.
Published: (2026)
Similar Items
-
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
by: Perera, Roberto, et al.
Published: (2024) -
Automated machine learning for physics-informed convolutional neural networks
by: Zhou, Wanyun, et al.
Published: (2024) -
Surface profile recovery from electromagnetic field with physics--informed neural networks
by: Chen, Yuxuan, et al.
Published: (2024) -
VW-PINNs: A volume weighting method for PDE residuals in physics-informed neural networks
by: Song, Jiahao, et al.
Published: (2024) -
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
by: Naderibeni, M., et al.
Published: (2024)