Materials
Materials
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DOI：
10.1111/j.1755-3768.1989.tb05299.x
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发表时间：
1989-07
影响因子：
3.4
通讯作者：
Sumit Sharma
中科院分区：
文献类型：
--
作者：
Sumit Sharma
生物软组织的刚度不仅取决于所施加的变形，而且还取决于变形速率。为了模拟这种类型的行为，传统的方法选择一个特定的时间相关的本构模型，并拟合其参数的实验数据。相反，现在的一个新趋势是提出一种基于机器学习的方法，可以同时发现解释给定数据的最佳模型和最佳参数。最近的研究表明，前馈本构神经网络可以鲁棒地发现超弹性材料的本构模型和参数。然而，前馈架构未能捕获粘弹性软组织的历史依赖性。在这里，我们结合了联合收割机的超弹性响应的前馈本构神经网络和准线性粘弹性理论的启发，粘性响应的递归神经网络。我们的新型流变信息网络架构使用前馈网络发现了与时间无关的初始应力，使用递归网络发现了与时间相关的松弛。我们使用被动骨骼肌的无侧限压缩松弛实验来训练和测试我们的组合网络，并将我们发现的模型与neo Hookean标准线性固体，先进的基于力学的模型以及没有力学知识的香草递归神经网络进行比较。我们证明，对于有限的实验数据，我们的新的组成性递归神经网络发现了满足基本物理原理的模型和参数，并很好地推广到看不见的数据。我们发现了一个Mooney-Rivlin型的两项初始储能函数，该函数在第一不变量中是线性的，在第二不变量中是二次的，刚度参数为0.60 kPa和0.55 kPa。𝐼我们还发现了一个Prony系列型弛豫函数的时间常数为0.362s，2.54s，和52.0s的系数为0.89，0.05，和0.03。我们新发现的模型在对未知数据的预测准确性方面优于neo Hookean标准线性实体和香草递归神经网络。我们的研究结果表明，本构递归神经网络可以自主发现模型和参数，最好地解释软粘弹性组织的实验数据。我们的源代码、数据和示例可以在https://github.com/LivingMatterLab上找到。
The stiffness of soft biological tissues not only depends on the applied deformation, but also on the deformation rate. To model this type of behavior, traditional approaches select a specific time-dependent constitutive model and fit its parameters to experimental data. Instead, a new trend now suggests a machine-learning based approach that simultaneously discovers both the best model and best parameters to explain given data. Recent studies have shown that feed-forward constitutive neural networks can robustly discover constitutive models and parameters for hyperelastic materials. However, feed-forward architectures fail to capture the history dependence of viscoelastic soft tissues. Here we combine a feed-forward constitutive neural network for the hyperelastic response and a recurrent neural network for the viscous response inspired by the theory of quasi-linear viscoelasticity. Our novel rheologically-informed network architecture discovers the time-independent initial stress using the feed-forward network and the time-dependent relaxation using the recurrent network. We train and test our combined network using unconfined compression relaxation experiments of passive skeletal muscle and compare our discovered model to a neo Hookean standard linear solid, to an advanced mechanics-based model, and to a vanilla recurrent neural network with no mechanics knowledge. We demonstrate that, for limited experimental data, our new constitutive recurrent neural network discovers models and parameters that satisfy basic physical principles and generalize well to unseen data. We discover a Mooney–Rivlin type two-term initial stored energy function that is linear in the first invariant 𝐼 1 and quadratic in the second invariant 𝐼 2 with stiffness parameters of 0.60 kPa and 0.55 kPa. We also discover a Prony-series type relaxation function with time constants of 0.362s, 2.54s, and 52.0s with coefficients of 0.89, 0.05, and 0.03. Our newly discovered model outperforms both the neo Hookean standard linear solid and the vanilla recurrent neural network in terms of prediction accuracy on unseen data. Our results suggest that constitutive recurrent neural networks can autonomously discover both model and parameters that best explain experimental data of soft viscoelastic tissues. Our source code, data, and examples are available at https://github.com/LivingMatterLab.