Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs)
Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs)
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DOI：
10.1007/s00397-022-01357-w
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发表时间：
2022-08-03
期刊：
影响因子：
2.3
通讯作者：
Jamali, Safa
中科院分区：
文献类型：
--
作者：
Saadat, Milad;Mahmoudabadbozchelou, Mohammadamin;Jamali, Safa
几十年来，无数的经验和现象学本构模型描述了观察到的复杂流体的不同流变性。由于这些本构模型在恢复不同流变响应方面的强度，允许数据自动选择适当本构关系的算法对流变学家非常感兴趣。在这里，我们提出了一种流变信息神经网络（RhINN），可以在最小用户干预的情况下，根据可用的实验数据进行稳健的模型选择。我们在不同复杂流体的一系列实验数据上训练了我们的RhINN，并表明它能够用最少的拟合参数为每个数据集找到合适的模型。最后，我们表明，在整个可访问的剪切速率上统一选择少数数据不会影响RhINN的准确性，而提供特定范围的数据（并省略其余部分）会导致错误的模型确定。
A myriad of empirical and phenomenological constitutive models that describe different observed rheologies of complex fluids have been developed over many decades. With each of these constitutive models' strength in recovering different rheological responses, algorithms that allow the data to automatically select the appropriate constitutive relations are of great interest to rheologists. Here, we present a rheology-informed neural network (RhINN) that enables robust model selection based on available experimental data with minimal user intervention. We train our RhINN on a series of experimental data for different complex fluids and show that it is capable of finding the appropriate model with the lowest number of fitting parameters for each data set. Finally, we show that uniform selection of a handful of data over the entire accessible shear rates does not affect the RhINN's accuracy, while providing a specific range of data (and omitting the rest) results in an erroneous model determination.