A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
Fuente:
arXiv
Guardado en:
| Autores principales: | Dick, Josef, Ko, Seungchan, Gia, Quoc Thong Le, Mustapha, Kassem, Park, Sanghyeon |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A simple modification to mitigate locking in conforming FEM for nearly incompressible elasticity
por: Mustapha, K., et al.
Publicado: (2024)
por: Mustapha, K., et al.
Publicado: (2024)
High-order QMC nonconforming FEMs for nearly incompressible planar stochastic elasticity equations
por: Dick, J., et al.
Publicado: (2024)
por: Dick, J., et al.
Publicado: (2024)
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
por: Ko, Seungchan, et al.
Publicado: (2024)
por: Ko, Seungchan, et al.
Publicado: (2024)
An $α$-robust and second-order accurate scheme for a subdiffusion equation
por: Mustapha, Kassem, et al.
Publicado: (2024)
por: Mustapha, Kassem, et al.
Publicado: (2024)
Bayesian inference calibration of the modulus of elasticity
por: Dick, J., et al.
Publicado: (2025)
por: Dick, J., et al.
Publicado: (2025)
Quasi-Monte Carlo sparse grid Galerkin finite element methods for linear elasticity equations with uncertainties
por: Clarke, M., et al.
Publicado: (2023)
por: Clarke, M., et al.
Publicado: (2023)
Time-fractional diffusion equations with randomness, and efficient numerical estimations of expected values
por: Dick, Josef, et al.
Publicado: (2024)
por: Dick, Josef, et al.
Publicado: (2024)
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
por: Heinlein, Alexander, et al.
Publicado: (2025)
por: Heinlein, Alexander, et al.
Publicado: (2025)
Multilevel domain decomposition-based architectures for physics-informed neural networks
por: Dolean, Victorita, et al.
Publicado: (2023)
por: Dolean, Victorita, et al.
Publicado: (2023)
M‐PINN: A mesh‐based physics‐informed neural network for linear elastic problems in solid mechanics
por: Lu Wang, et al.
Publicado: (2024)
por: Lu Wang, et al.
Publicado: (2024)
Engineering application of physics-informed neural networks for Saint-Venant torsion
por: Jo, Su Yeong, et al.
Publicado: (2025)
por: Jo, Su Yeong, et al.
Publicado: (2025)
Stress-hybrid virtual element method on six-noded triangular meshes for compressible and nearly-incompressible linear elasticity
por: Chen, Alvin, et al.
Publicado: (2024)
por: Chen, Alvin, et al.
Publicado: (2024)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
por: Liu, Ye, et al.
Publicado: (2024)
por: Liu, Ye, et al.
Publicado: (2024)
Quasi-Monte Carlo finite element approximation of the Navier-Stokes equations with initial data modeled by log-normal random fields
por: Ko, Seungchan, et al.
Publicado: (2022)
por: Ko, Seungchan, et al.
Publicado: (2022)
Partial‐differential‐algebraic equations of nonlinear dynamics by physics‐informed neural‐network: (I) Operator splitting and framework assessment
por: Loc Vu‐Quoc, et al.
Publicado: (2024)
por: Loc Vu‐Quoc, et al.
Publicado: (2024)
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
por: Kopaničáková, Alena, et al.
Publicado: (2023)
por: Kopaničáková, Alena, et al.
Publicado: (2023)
Astral: training physics-informed neural networks with error majorants
por: Fanaskov, Vladimir, et al.
Publicado: (2024)
por: Fanaskov, Vladimir, et al.
Publicado: (2024)
Vector-Valued Gaussian Processes for Approximating Divergence- or Rotation-free Vector Fields
por: Gia, Quoc Thong Le, et al.
Publicado: (2025)
por: Gia, Quoc Thong Le, et al.
Publicado: (2025)
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
por: Hong, Youngjoon, et al.
Publicado: (2024)
por: Hong, Youngjoon, et al.
Publicado: (2024)
Sobolev Approximation of Deep ReLU Networks in Log-Barron Space
por: Song, Changhoon, et al.
Publicado: (2026)
por: Song, Changhoon, et al.
Publicado: (2026)
An inherent regularization approach to parameter-free preconditioning for nearly incompressible linear poroelasticity and elasticity
por: Huang, Weizhang, et al.
Publicado: (2025)
por: Huang, Weizhang, et al.
Publicado: (2025)
Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
por: Heinlein, Alexander, et al.
Publicado: (2024)
por: Heinlein, Alexander, et al.
Publicado: (2024)
Removing the mask -- reconstructing a scalar field on the sphere from a masked field
por: Hamann, Jan, et al.
Publicado: (2023)
por: Hamann, Jan, et al.
Publicado: (2023)
Multiprecision computing for multistage fractional physics-informed neural networks
por: Xue, Na, et al.
Publicado: (2025)
por: Xue, Na, et al.
Publicado: (2025)
Weak and entropy physics-informed neural networks for conservation laws
por: Oubarka, Ismail, et al.
Publicado: (2026)
por: Oubarka, Ismail, et al.
Publicado: (2026)
Fast training of accurate physics-informed neural networks without gradient descent
por: Datar, Chinmay, et al.
Publicado: (2024)
por: Datar, Chinmay, et al.
Publicado: (2024)
A mixed FEM for a time-fractional Fokker-Planck model
por: Karaa, Samir, et al.
Publicado: (2023)
por: Karaa, Samir, et al.
Publicado: (2023)
Pseudo-differential-enhanced physics-informed neural networks
por: Gracyk, Andrew
Publicado: (2026)
por: Gracyk, Andrew
Publicado: (2026)
Error estimates of physics-informed neural networks for approximating Boltzmann equation
por: Abdo, Elie, et al.
Publicado: (2024)
por: Abdo, Elie, et al.
Publicado: (2024)
Four-field mixed finite elements for incompressible nonlinear elasticity
por: Badia, Santiago, et al.
Publicado: (2026)
por: Badia, Santiago, et al.
Publicado: (2026)
A matrix preconditioning framework for physics-informed neural networks based on adjoint method
por: Song, Jiahao, et al.
Publicado: (2025)
por: Song, Jiahao, et al.
Publicado: (2025)
Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation
por: Rahman, Mohammad Mahabubur, et al.
Publicado: (2025)
por: Rahman, Mohammad Mahabubur, et al.
Publicado: (2025)
Exact and approximate error bounds for physics-informed neural networks
por: Chantada, Augusto T., et al.
Publicado: (2024)
por: Chantada, Augusto T., et al.
Publicado: (2024)
Towards optimal hierarchical training of neural networks
por: Feischl, Michael, et al.
Publicado: (2024)
por: Feischl, Michael, et al.
Publicado: (2024)
Challenges in automatic differentiation and numerical integration in physics-informed neural networks modelling
por: Daněk, Josef, et al.
Publicado: (2024)
por: Daněk, Josef, et al.
Publicado: (2024)
Optimal time sampling in physics-informed neural networks
por: Turinici, Gabriel
Publicado: (2024)
por: Turinici, Gabriel
Publicado: (2024)
A variationally consistent membrane wrinkling model based on spectral decomposition of the stress tensor
por: Zhang, Daobo, et al.
Publicado: (2025)
por: Zhang, Daobo, et al.
Publicado: (2025)
A variationally consistent membrane wrinkling model based on spectral decomposition of the strain tensor
por: Zhang, Daobo, et al.
Publicado: (2024)
por: Zhang, Daobo, et al.
Publicado: (2024)
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
por: Guo, Yuan, et al.
Publicado: (2025)
por: Guo, Yuan, et al.
Publicado: (2025)
IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
por: Zheng, Jiachun, et al.
Publicado: (2025)
por: Zheng, Jiachun, et al.
Publicado: (2025)
Ejemplares similares
-
A simple modification to mitigate locking in conforming FEM for nearly incompressible elasticity
por: Mustapha, K., et al.
Publicado: (2024) -
High-order QMC nonconforming FEMs for nearly incompressible planar stochastic elasticity equations
por: Dick, J., et al.
Publicado: (2024) -
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
por: Ko, Seungchan, et al.
Publicado: (2024) -
An $α$-robust and second-order accurate scheme for a subdiffusion equation
por: Mustapha, Kassem, et al.
Publicado: (2024) -
Bayesian inference calibration of the modulus of elasticity
por: Dick, J., et al.
Publicado: (2025)