Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models
Fuente:
arXiv
Saved in:
| Main Authors: | Difonzo, Fabio V., Lopez, Luciano, Pellegrino, Sabrina F. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Convergence Analysis of a Spectral Numerical Method for a Peridynamic Formulation of Richards' Equation
by: Difonzo, Fabio V., et al.
Published: (2023)
by: Difonzo, Fabio V., et al.
Published: (2023)
Inverse Physics-Informed Neural Networks for transport models in porous materials
by: Berardi, Marco, et al.
Published: (2024)
by: Berardi, Marco, et al.
Published: (2024)
Nonnegative moment coordinates on finite element geometries
by: Dieci, Luca, et al.
Published: (2023)
by: Dieci, Luca, et al.
Published: (2023)
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)
by: Difonzo, Fabio V., et al.
Published: (2022)
A Randomized Runge-Kutta Method for time-irregular delay differential equations
by: Difonzo, Fabio V., et al.
Published: (2024)
by: Difonzo, Fabio V., et al.
Published: (2024)
Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions
by: Wang, Jihong, et al.
Published: (2025)
by: Wang, Jihong, et al.
Published: (2025)
On the Stability and Convergence of Physics Informed Neural Networks
by: Gazoulis, Dimitrios, et al.
Published: (2023)
by: Gazoulis, Dimitrios, et al.
Published: (2023)
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)
by: Dzimah, Salvador K., et al.
Published: (2026)
An Efficient Explicit-Implicit Adaptive Method for Peridynamic Modelling of Quasi-Static Fracture Formation and Evolution
by: Hu, Shiwei, et al.
Published: (2024)
by: Hu, Shiwei, et al.
Published: (2024)
Coupling Physics Informed Neural Networks with External Solvers
by: Halder, Rahul, et al.
Published: (2025)
by: Halder, Rahul, et al.
Published: (2025)
Physics Informed Neural Networks for heat conduction with phase change
by: Madir, Bahae-Eddine, et al.
Published: (2024)
by: Madir, Bahae-Eddine, et al.
Published: (2024)
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
by: Matsubara, Takashi, et al.
Published: (2023)
by: Matsubara, Takashi, et al.
Published: (2023)
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)
by: Liu, Songming, et al.
Published: (2024)
A Physics-Informed Neural Network approach for compartmental epidemiological models
by: Millevoi, Caterina, et al.
Published: (2023)
by: Millevoi, Caterina, et al.
Published: (2023)
Optimizing Variational Physics-Informed Neural Networks Using Least Squares
by: Uriarte, Carlos, et al.
Published: (2024)
by: Uriarte, Carlos, et al.
Published: (2024)
Extended Interface Physics-Informed Neural Networks Method for Moving Interface Problems
by: Bi, Ran, et al.
Published: (2025)
by: Bi, Ran, et al.
Published: (2025)
The Ill-Posed Foundations of Physics-Informed Neural Networks and Their Finite-Difference Variants
by: Langer, Andreas
Published: (2026)
by: Langer, Andreas
Published: (2026)
Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality
by: Zhou, Qijia, et al.
Published: (2026)
by: Zhou, Qijia, et al.
Published: (2026)
Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)
by: Xu, Xianliang, et al.
Published: (2024)
Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)
by: Zhou, Chenhong, et al.
Published: (2025)
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
by: Andre-Sloan, Sebastien, et al.
Published: (2026)
PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces
by: Jain, Pranav, et al.
Published: (2026)
by: Jain, Pranav, et al.
Published: (2026)
Runge-Kutta Physics Informed Neural Networks: Formulation and Analysis
by: Akrivis, Georgios, et al.
Published: (2024)
by: Akrivis, Georgios, et al.
Published: (2024)
Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
by: Li, Guojie, et al.
Published: (2026)
by: Li, Guojie, et al.
Published: (2026)
DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
by: Yang, Zijiang, et al.
Published: (2022)
by: Yang, Zijiang, et al.
Published: (2022)
Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
by: Zhang, Tao, et al.
Published: (2026)
by: Zhang, Tao, et al.
Published: (2026)
Solving Poisson Problems in Polygonal Domains with Singularity Enriched Physics Informed Neural Networks
by: Hu, Tianhao, et al.
Published: (2023)
by: Hu, Tianhao, et al.
Published: (2023)
E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)
by: Jacob, Bruno, et al.
Published: (2025)
Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)
by: Liu, Li, et al.
Published: (2022)
PhysicsSolver: Transformer-Enhanced Physics-Informed Neural Networks for Forward and Forecasting Problems in Partial Differential Equations
by: Zhu, Zhenyi, et al.
Published: (2025)
by: Zhu, Zhenyi, et al.
Published: (2025)
Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology
by: Sierpe, Moises, et al.
Published: (2025)
by: Sierpe, Moises, et al.
Published: (2025)
Solution of Advection Equation with Discontinuous Initial and Boundary Conditions via Physics-Informed Neural Networks
by: Khosravi, Omid, et al.
Published: (2026)
by: Khosravi, Omid, et al.
Published: (2026)
Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
by: Nohra, Michel, et al.
Published: (2024)
by: Nohra, Michel, et al.
Published: (2024)
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)
by: Zhou, Wei, et al.
Published: (2024)
Learning from Integral Losses in Physics Informed Neural Networks
by: Saleh, Ehsan, et al.
Published: (2023)
by: Saleh, Ehsan, et al.
Published: (2023)
Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025)
by: Mohammad-Djafari, Ali
Published: (2025)
Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)
by: Guan, Jiajing, et al.
Published: (2024)
Leveraging Lie Group Symmetries to Enhance Physics-Informed Neural Networks for the Fundamental Solution of Linear PDEs
by: Jiao, Xiaopei, et al.
Published: (2024)
by: Jiao, Xiaopei, et al.
Published: (2024)
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)
by: Gao, Yihang, et al.
Published: (2022)
A Unified Weighted-Loss Physics-Informed Neural Network for Boundary Layer Problems in Singularly Perturbed PDEs
by: Hu, Wei-Fan, et al.
Published: (2026)
by: Hu, Wei-Fan, et al.
Published: (2026)
Similar Items
-
Convergence Analysis of a Spectral Numerical Method for a Peridynamic Formulation of Richards' Equation
by: Difonzo, Fabio V., et al.
Published: (2023) -
Inverse Physics-Informed Neural Networks for transport models in porous materials
by: Berardi, Marco, et al.
Published: (2024) -
Nonnegative moment coordinates on finite element geometries
by: Dieci, Luca, et al.
Published: (2023) -
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022) -
A Randomized Runge-Kutta Method for time-irregular delay differential equations
by: Difonzo, Fabio V., et al.
Published: (2024)