We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.

中文翻译：

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水凝胶本构模型中参数确定的机器学习嵌入式方法及潜在应用

我们提出了一种在水凝胶结构模型中确定参数的机器学习嵌入式方法。发现开发的水凝胶溶胀类逻辑回归算法允许我们根据已知的溶胀比和化学势确定拟合参数。我们还提出了类似神经网络的算法，该算法由于其自身的特性，可以随着层的加深而更快地收敛。然后，我们开发了用于实验应用目的的单轴载荷下水凝胶的类似神经网络的算法。最后，我们提出了几种机器学习嵌入式水凝胶的潜在应用，这将为基于机器学习的水凝胶研究提供方向。