Abstract

This article compares machine learning methods and a numerical method of determination of the doped lubricating layer with experimental data. Based on the sweep method, the one-dimensional Fourier heat equation with boundary and initial conditions is solved. As a result of comparing numerical and predictive data with experiments, it can be concluded that machine learning models are better at predicting results compared to numerical data.

中文翻译：

---

掺杂润滑层温度测定的数值方法和机器学习方法与实验数据的比较分析

摘要

本文将机器学习方法和确定掺杂润滑层的数值方法与实验数据进行了比较。基于扫描法，求解具有边界和初始条件的一维傅立叶热方程。通过将数值数据和预测数据与实验进行比较，可以得出结论，与数值数据相比，机器学习模型更擅长预测结果。