{"title":"流可视化中的深度学习方法","authors":"Liu, Can, Jiang, Ruike, Wei, Datong, Yang, Changhe, Li, Yanda, Wang, Fang, Yuan, Xiaoru","doi":"10.1186/s42774-022-00113-1","DOIUrl":null,"url":null,"abstract":"With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":"113 5-6","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Deep learning approaches in flow visualization\",\"authors\":\"Liu, Can, Jiang, Ruike, Wei, Datong, Yang, Changhe, Li, Yanda, Wang, Fang, Yuan, Xiaoru\",\"doi\":\"10.1186/s42774-022-00113-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.\",\"PeriodicalId\":33737,\"journal\":{\"name\":\"Advances in Aerodynamics\",\"volume\":\"113 5-6\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2022-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Aerodynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s42774-022-00113-1\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Aerodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s42774-022-00113-1","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods. We reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these approaches. We also analyzed the prospects of the development of flow visualization with the help of deep learning.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
