핵심 개념
Learning constitutive laws and microstructure of biotissues using Heterogeneous Peridynamic Neural Operators.
초록
The content introduces the Heterogeneous Peridynamic Neural Operators (HeteroPNO) approach for data-driven constitutive modeling of heterogeneous anisotropic materials, focusing on biotissues. The method aims to learn a nonlocal constitutive law and material microstructure from loading field-displacement field measurements. The two-phase learning approach involves training a homogeneous constitutive law and then reinitializing it with a fiber orientation field for each material point. The HeteroPNO architecture ensures objective material models with physical interpretability, capturing anisotropy and heterogeneity in biological tissues. The approach is validated using digital image correlation data on tissue samples, demonstrating consistency with observations from imaging techniques. The framework provides predictions for displacement and stress fields for new loading instances.
Directory:
- Introduction
- Background
- Peridynamic Theory
- Nonlocal Neural Operators
- Heterogeneous Peridynamic Neural Operators
- Mathematical Formulation
- Machine learning algorithm
- Verification on Synthetic Dataset
- Data preparation
- Learning the constitutive law and microstructure
- Application on DIC measurements of Bio-tissues
- Summary and future directions
통계
Human tissues are highly organized structures with specific collagen fiber arrangements.
The HeteroPNO approach aims to learn a nonlocal constitutive law and material microstructure.
The method involves a two-phase learning approach for training the constitutive law and fiber orientation field.
The HeteroPNO architecture captures anisotropy and heterogeneity in biological tissues.
The framework provides displacement and stress field predictions for new loading instances.
인용구
"Our HeteroPNO architecture can learn a constitutive model for a biological tissue with anisotropic heterogeneous response."