Can machine learning accelerate soft material parameter identification from complex mechanical test data?
Can machine learning accelerate soft material parameter identification from complex mechanical test data?
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DOI：
10.1007/s10237-022-01631-z
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发表时间：
2022-10-13
影响因子：
3.5
通讯作者：
Rausch, Manuel K.
中科院分区：
文献类型：
--
作者：
Kakaletsis, Sotirios;Lejeune, Emma;Rausch, Manuel K.
识别软材料的本构参数通常需要异构机械测试模式，例如简单剪切。反过来，解释所产生的复杂变形需要使用迭代调用前向有限元解的逆策略。过去，我们发现重复解决非平凡边值问题的成本可能非常昂贵。在当前的工作中，我们利用之前通过实验得出的机械测试数据来探索替代方法。具体来说，我们研究基于机器学习的方法是否可以加速根据我们的机械测试数据识别材料参数的过程。为此，我们采取两种不同的策略。在第一个策略中，我们用基于机器学习的元模型替换迭代优化框架内的前向有限元模拟。在这里，我们探索高斯过程回归和神经网络元模型。在第二种策略中，我们放弃迭代优化框架，并使用独立的神经网络直接根据实验结果预测整个材料参数集。我们首先通过对血凝块（一种各向同性的均质材料）进行简单的剪切实验来评估这两种方法。接下来，我们针对右心室心肌（一种各向异性的异质材料）的简单剪切和单轴加载实验评估这两种方法。我们发现，用元模型代替正向有限元模拟可以显着加速参数识别过程，在血凝块的情况下取得优异的结果，在右心室心肌的情况下取得令人满意的结果。另一方面，我们发现用神经网络替换整个优化框架产生了不令人满意的结果，特别是对于右心室心肌。总的来说，我们工作的重要性源于提供一个基线示例，展示机器学习如何加速从复杂的机械数据中识别软材料的材料参数的过程，并提供一个开放访问的实验和模拟数据集，该数据集可以作为其他有兴趣将机器学习技术应用于软组织生物力学的人的基准数据集。
Identifying the constitutive parameters of soft materials often requires heterogeneous mechanical test modes, such as simple shear. In turn, interpreting the resulting complex deformations necessitates the use of inverse strategies that iteratively call forward finite element solutions. In the past, we have found that the cost of repeatedly solving non-trivial boundary value problems can be prohibitively expensive. In this current work, we leverage our prior experimentally derived mechanical test data to explore an alternative approach. Specifically, we investigate whether a machine learning-based approach can accelerate the process of identifying material parameters based on our mechanical test data. Toward this end, we pursue two different strategies. In the first strategy, we replace the forward finite element simulations within an iterative optimization framework with a machine learning-based metamodel. Here, we explore both Gaussian process regression and neural network metamodels. In the second strategy, we forgo the iterative optimization framework and use a stand alone neural network to predict the entire material parameter set directly from experimental results. We first evaluate both approaches with simple shear experiments on blood clot, an isotropic, homogeneous material. Next, we evaluate both approaches against simple shear and uniaxial loading experiments on right ventricular myocardium, an anisotropic, heterogeneous material. We find that replacing the forward finite element simulations with metamodels significantly accelerates the parameter identification process with excellent results in the case of blood clot, and with satisfying results in the case of right ventricular myocardium. On the other hand, we find that replacing the entire optimization framework with a neural network yielded unsatisfying results, especially for right ventricular myocardium. Overall, the importance of our work stems from providing a baseline example showing how machine learning can accelerate the process of material parameter identification for soft materials from complex mechanical data, and from providing an open access experimental and simulation dataset that may serve as a benchmark dataset for others interested in applying machine learning techniques to soft tissue biomechanics.