{"title":"人工神经网络对拉伸片引起的三元混合纳米流体流动的计算评估：机器学习方法的意义","authors":"Imad Khan, M. Waleed Ahmed Khan","doi":"10.1080/19942060.2024.2411786","DOIUrl":null,"url":null,"abstract":"Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural network...","PeriodicalId":50524,"journal":{"name":"Engineering Applications of Computational Fluid Mechanics","volume":"6 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach\",\"authors\":\"Imad Khan, M. Waleed Ahmed Khan\",\"doi\":\"10.1080/19942060.2024.2411786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural network...\",\"PeriodicalId\":50524,\"journal\":{\"name\":\"Engineering Applications of Computational Fluid Mechanics\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Computational Fluid Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19942060.2024.2411786\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Computational Fluid Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19942060.2024.2411786","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach
Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural network...
期刊介绍：
The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.
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