This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al₂O₃, TiO₂, SiO₂, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R² = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past.

中文翻译：

---

使用称为高斯过程回归方法的连接智能模型估计纳米流体的物理性质

这项工作旨在开发一个强大的机器学习模型，用于基于影响它们的最重要的输入参数来预测包括 Al ₂ O ₃、TiO ₂、SiO ₂ 、CuO、SiC 和 Ag 在内的纳米粒子 (NPs) 的相对粘度纳米粒子的尺寸、浓度、界面层厚度和强度特性。为了在本研究中开发一个全面的人工智能模型，收集了 69 个数据样本。为此，利用了具有四个基本函数内核（Matern、平方指数、指数和有理二次）的高斯过程回归方法。发现 Matern 的R ^{2优于其他模型} = 0.987，MARE (%) = 6.048，RMSE = 0.0577，STD = 0.0574。这种精确而简单的模型可以很好地替代过去复杂的热力学、数学分析模型。