使用 CatBoost 梯度提升算法预测聚合物流变参数

IF 0.58 Q4 Materials Science

Polymer Science, Series D Pub Date : 2024-06-14 DOI:10.1134/S199542122370020X

A. S. Chepurnenko, T. N. Kondratieva, T. R. Deberdeev, V. F. Akopyan, A. A. Avakov, V. S. Chepurnenko

{"title":"使用 CatBoost 梯度提升算法预测聚合物流变参数","authors":"A. S. Chepurnenko, T. N. Kondratieva, T. R. Deberdeev, V. F. Akopyan, A. A. Avakov, V. S. Chepurnenko","doi":"10.1134/S199542122370020X","DOIUrl":null,"url":null,"abstract":"<p>The article discusses the problem of determining the rheological parameters of polymers from stress relaxation curves using the CatBoost machine learning algorithm. The model is trained on theoretical curves constructed using the nonlinear Maxwell–Gurevich equation. Comparisons are made with other methods, including the classical algorithm, nonlinear optimization methods, and artificial neural networks.</p>","PeriodicalId":741,"journal":{"name":"Polymer Science, Series D","volume":"17 1","pages":"121 - 128"},"PeriodicalIF":0.5800,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Rheological Parameters of Polymers Using the CatBoost Gradient Boosting Algorithm\",\"authors\":\"A. S. Chepurnenko, T. N. Kondratieva, T. R. Deberdeev, V. F. Akopyan, A. A. Avakov, V. S. Chepurnenko\",\"doi\":\"10.1134/S199542122370020X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The article discusses the problem of determining the rheological parameters of polymers from stress relaxation curves using the CatBoost machine learning algorithm. The model is trained on theoretical curves constructed using the nonlinear Maxwell–Gurevich equation. Comparisons are made with other methods, including the classical algorithm, nonlinear optimization methods, and artificial neural networks.</p>\",\"PeriodicalId\":741,\"journal\":{\"name\":\"Polymer Science, Series D\",\"volume\":\"17 1\",\"pages\":\"121 - 128\"},\"PeriodicalIF\":0.5800,\"publicationDate\":\"2024-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polymer Science, Series D\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S199542122370020X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Materials Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polymer Science, Series D","FirstCategoryId":"1","ListUrlMain":"https://link.springer.com/article/10.1134/S199542122370020X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Materials Science","Score":null,"Total":0}

引用次数: 0

摘要

文章讨论了利用 CatBoost 机器学习算法从应力松弛曲线确定聚合物流变参数的问题。该模型是在使用非线性 Maxwell-Gurevich 方程构建的理论曲线上进行训练的。该模型与其他方法进行了比较，包括经典算法、非线性优化方法和人工神经网络。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Prediction of Rheological Parameters of Polymers Using the CatBoost Gradient Boosting Algorithm

查看原文本刊更多论文

Prediction of Rheological Parameters of Polymers Using the CatBoost Gradient Boosting Algorithm

The article discusses the problem of determining the rheological parameters of polymers from stress relaxation curves using the CatBoost machine learning algorithm. The model is trained on theoretical curves constructed using the nonlinear Maxwell–Gurevich equation. Comparisons are made with other methods, including the classical algorithm, nonlinear optimization methods, and artificial neural networks.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Polymer Science, Series D Materials Science-Polymers and Plastics

CiteScore

0.80

自引率

0.00%

发文量

期刊介绍： Polymer Science, Series D publishes useful description of engineering developments that are related to the preparation and application of glues, compounds, sealing materials, and binding agents, articles on the adhesion theory, prediction of the strength of adhesive joints, methods for the control of their properties, synthesis, and methods of structural modeling of glued joints and constructions, original articles with new scientific results, analytical reviews of the modern state in the field.