A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models
A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models
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流变学家从本构模型中数据驱动恢复复杂流体参数的指南
DOI：
10.1039/d3dd00036b
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jamali, Safa
中科院分区：
文献类型：
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作者：
Saadat, Milad;Mangal, Deepak;Jamali, Safa
流变学信息神经网络（rhinn）最近作为数据驱动平台被广泛应用于求解流变学相关微分方程。虽然可以使用rhinn以正向或逆的方式解决不同的本构方程，但它们的能力严格依赖于数据类型和嵌入其结构中的模型的选择。在这里，研究了rhinn的一般适用性，以及模型选择、神经网络本身参数和手头数据类型之间的相互作用。为此，一个RhINN由一系列触变弹粘塑性（TEVP）本构模型提供信息，并研究了其从应力增长和振荡剪切流协议中准确恢复模型参数的能力。我们观察到，通过简化本构模型，在参数恢复精度和计算速度方面提高了RhINN的收敛性，而过度简化模型则不利于精度。此外，几个超参数，如学习率、激活函数、拟合参数的初始条件和误差启发式，在旨在使用rhinn提高参数恢复时，应该放在检查表的顶部。最后，给定的数据形式起着关键作用，当一组实验作为任意一种流动协议的给定数据时，没有观察到收敛性。当使用rhinn进行参数恢复时，参数的范围也是一个限制因素，并且对本构模型进行特别修改可以是微不足道的补救措施，以保证在恢复具有大值的拟合参数时收敛。
Rheology-informed neural networks (RhINNs) have recently been popularized as data-driven platforms for solving rheologically relevant differential equations. While RhINNs can be employed to solve different constitutive equations of interest in a forward or inverse manner, their ability to do so strictly depends on the type of data and the choice of models embedded within their structure. Here, the applicability of RhINNs in general, and the interplay between the choice of models, parameters of the neural network itself, and the type of data at hand are studied. To do so, a RhINN is informed by a series of thixotropic elasto-visco-plastic (TEVP) constitutive models, and its ability to accurately recover model parameters from stress growth and oscillatory shear flow protocols is investigated. We observed that by simplifying the constitutive model, RhINN convergence is improved in terms of parameter recovery accuracy and computation speed while over-simplifying the model is detrimental to accuracy. Moreover, several hyperparameters, e.g., the learning rate, activation function, initial conditions for the fitting parameters, and error heuristics, should be at the top of the checklist when aiming to improve parameter recovery using RhINNs. Finally, the given data form plays a pivotal role, and no convergence is observed when one set of experiments is used as the given data for either of the flow protocols. The range of parameters is also a limiting factor when employing RhINNs for parameter recovery, and ad hoc modifications to the constitutive model can be trivial remedies to guarantee convergence when recovering fitting parameters with large values.