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A generic physics-informed neural network-based constitutive model for soft biological tissues

机译：一种用于软生物组织的通用物理信息基于神经网络的本构模型

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摘要

Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machine learning (ML) techniques, trained by supervised learning, are powerful in building a direct linkage between input and output, which can be the strain and stress relation in constitutive modeling. In this study, we developed a novel generic physics-informed neural network material (NNMat) model which employs a hierarchical learning strategy by following the steps: (1) establishing constitutive laws to describe general characteristic behaviors of a class of materials; (2) determining constitutive parameters for an individual subject. A novel neural network structure was proposed which has two sets of parameters: (1) a class parameter set for characterizing the general elastic properties; and (2) a subject parameter set (three parameters) for describing individual material response. The trained NNMat model may be directly adopted for a different subject without re-training the class parameters, and only the subject parameters are considered as constitutive parameters. Skip connections are utilized in the neural network to facilitate hierarchical learning. A convexity constraint was imposed to the NNMat model to ensure that the constitutive model is physically relevant. The NNMat model was trained, cross-validated and tested using biaxial testing data of 63 ascending thoracic aortic aneurysm tissue samples, which was compared to expert -constructed models (Holzapfel-Gasser-Ogden, Gasser-Ogden-Holzapfel, and four-fiber families) using the same fitting and testing procedure. Our results demonstrated that the NNMat model has a significantly better performance in both fitting (R2 value of 0.9632 vs 0.9019, p=0.0053) and testing (R2 value of 0.9471 vs 0.8556, p=0.0203) than the Holzapfel-Gasser-Ogden model. The proposed NNMat model provides a convenient and general methodology for constitutive modeling. (C) 2020 Elsevier B.V. All rights reserved.
机译：本构型建模是生物软组织力学行为应力分析的基石。最近，已经表明，通过监督学习训练的机器学习（ML）技术是强大的，在输入和输出之间建立直接联动，这可以是本构型建模中的应变和应力关系。在这项研究中，我们开发了一种新型通用物理知识的神经网络材料（NNMAT）模型，遵​​循以下步骤：（1）建立一类材料的一般特征行为的组成术法; （2）确定个体主体的组成型参数。提出了一种新型神经网络结构，其具有两组参数：（1）用于表征普通弹性特性的类参数集; （2）用于描述单个材料响应的主题参数集（三个参数）。可以在不重新训练类参数的情况下直接采用培训的NNMAT模型，并且仅将主题参数视为本构参数。跳过连接在神经网络中使用以促进分层学习。对NNMAT模型施加了凸起约束，以确保本构模型是物理相关的。使用63个升胸主动脉动脉瘤组织样本的双轴测试数据进行培训，交叉验证和测试，与专家组织模型（Holzapfel-Gasser-Ogden，Gasser-Ogden-Holzapfel和四纤维家庭进行比较。 ）使用相同的拟合和测试程序。我们的结果表明，NNMAT模型在拟合（R2值为0.9632 Vs 0.9019，P = 0.0053）和测试（R2值为0.9471 Vs 0.8556，P = 0.0203）中具有明显更好的性能。所提出的NNMAT模型为本构型建模提供了一种方便和一般的方法。 （c）2020 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《Computer Methods in Applied Mechanics and Engineering》 |2020年第2期|113402.1-113402.17|共17页
  • 作者

    Liu Minliang; Liang Liang; Sun Wei;

  • 作者单位

    Georgia Inst Technol Tissue Mech Lab Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA|Emory Univ Technol Enterprise Pk Room 206 387 Technol Circle Atlanta GA 30313 USA;

    Univ Miami Dept Comp Sci Coral Gables FL 33124 USA;

    Georgia Inst Technol Tissue Mech Lab Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA|Emory Univ Technol Enterprise Pk Room 206 387 Technol Circle Atlanta GA 30313 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Constitutive modeling; Hyperelastic material; Machine learning; Neural network;

    机译：本构型建模;超弹性材料;机器学习;神经网络;

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