A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917063595917312 |
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| author | Konale, Aditya Srivastava, Vikas |
| author_facet | Konale, Aditya Srivastava, Vikas |
| contents | Accurate computational modeling of damage and fracture remains a central challenge in solid mechanics. The finite element method (FEM) is widely used for numerical modeling of fracture problems; however, classical damage models without gradient regularization yield mesh-dependent and usually inaccurate predictions. The use of gradient damage with FEM improves numerical robustness but introduces significant mathematical and numerical implementation complexities. Physics-informed neural networks (PINNs) can encode the governing partial differential equations, boundary conditions, and constitutive models into the loss functions, offering a new method for fracture modeling. Prior applications of PINNs have been limited to small-strain problems and have incorporated gradient damage formulation without a critical evaluation of its necessity. Since PINNs in their basic form are meshless, this work presents a PINN framework for modeling fracture in elastomers undergoing large deformation without the gradient damage formulation. The PINN implementation here does not require training data and utilizes the collocation method to formulate physics-informed loss functions. We have validated the PINN's predictions for various defect configurations using benchmark solutions obtained from FEM with gradient damage formulation. The crack paths obtained using the PINN are approximately insensitive to the collocation point distribution. This study offers new insights into the feasibility of using PINNs without gradient damage and suggests a simplified and efficient computational modeling strategy for fracture problems. The PINN's performance has been evaluated through systematic variations in key neural network parameters to provide an assessment and guidance for future applications. The results motivate the extension of PINN-based approaches to a broader class of materials and damage models in mechanics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment Konale, Aditya Srivastava, Vikas Soft Condensed Matter Accurate computational modeling of damage and fracture remains a central challenge in solid mechanics. The finite element method (FEM) is widely used for numerical modeling of fracture problems; however, classical damage models without gradient regularization yield mesh-dependent and usually inaccurate predictions. The use of gradient damage with FEM improves numerical robustness but introduces significant mathematical and numerical implementation complexities. Physics-informed neural networks (PINNs) can encode the governing partial differential equations, boundary conditions, and constitutive models into the loss functions, offering a new method for fracture modeling. Prior applications of PINNs have been limited to small-strain problems and have incorporated gradient damage formulation without a critical evaluation of its necessity. Since PINNs in their basic form are meshless, this work presents a PINN framework for modeling fracture in elastomers undergoing large deformation without the gradient damage formulation. The PINN implementation here does not require training data and utilizes the collocation method to formulate physics-informed loss functions. We have validated the PINN's predictions for various defect configurations using benchmark solutions obtained from FEM with gradient damage formulation. The crack paths obtained using the PINN are approximately insensitive to the collocation point distribution. This study offers new insights into the feasibility of using PINNs without gradient damage and suggests a simplified and efficient computational modeling strategy for fracture problems. The PINN's performance has been evaluated through systematic variations in key neural network parameters to provide an assessment and guidance for future applications. The results motivate the extension of PINN-based approaches to a broader class of materials and damage models in mechanics. |
| title | A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2507.07272 |