Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks

Song, Siyuan; Jin, Hanxun

doi:10.1039/d4sm00001c

Soft Matter

2024

DOI: 10.1039/d4sm00001c

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Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks

Siyuan Song,

Hanxun Jin

Abstract: A robust PINN-based framework was developed to identify material parameters for hyperelastic soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions.

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Cited by 3 publications

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A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

Zhu,

Xue,

Liu

2024

Appl. Math. Mech.-Engl. Ed.

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A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

Zhu,

Xue,

Liu

2024

Appl. Math. Mech.-Engl. Ed.

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Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Hu,

Qi,

Chao

2024

Thin-Walled Structures

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Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

Wang,

Li,

et al. 2024

Applied Sciences

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This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints. Different from traditional neural networks for solving partial differential equations, the proposed method uses a deep neural operator network to indirectly construct the mapping relationship between the variable functions and solution functions. This approach makes full use of the hidden information between the variable functions and independent variables. The process whereby the model captures incredibly complex and highly nonlinear relationships is simplified, thereby making network learning easier and enhancing the extraction of information about the independent variables in partial differential systems. In terms of solving partial differential equations, we verify that the multi-step physics-informed deep operator neural network markedly improves the solution accuracy compared with a traditional physics-informed deep neural operator network, especially when the problem involves complex physical phenomena with large gradient changes.

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Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks

Abstract: A robust PINN-based framework was developed to identify material parameters for hyperelastic soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions.

Cited by 3 publications

References 62 publications

A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

A transfer learning enhanced physics-informed neural network for parameter identification in soft materials

Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

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