Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

超弹性材料 弹性(物理) 人工神经网络 计算机科学 实验数据 非线性系统 人工智能 材料科学 机器学习 物理 数学 复合材料 统计 量子力学
作者
Wensi Wu,Mitchell Daneker,Kevin T. Turner,Matthew A. Jolley,Lu Lu
出处
期刊:Small methods [Wiley]
被引量:1
标识
DOI:10.1002/smtd.202400620
摘要

Abstract The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.
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