{"title":"复杂系统的神经降尺度：通过神经算子从大尺度到小尺度","authors":"Pengyu Lai, Jing Wang, Rui Wang, Dewu Yang, Haoqi Fei, Yihe Chen, Hui Xu","doi":"10.1080/19942060.2024.2399672","DOIUrl":null,"url":null,"abstract":"Researchers have long been working on interpreting and predicting the dynamics of complex systems in various fields. Conventional methods including full-scale simulations and reduced-order models a...","PeriodicalId":50524,"journal":{"name":"Engineering Applications of Computational Fluid Mechanics","volume":"28 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural downscaling for complex systems: from large-scale to small-scale by neural operator\",\"authors\":\"Pengyu Lai, Jing Wang, Rui Wang, Dewu Yang, Haoqi Fei, Yihe Chen, Hui Xu\",\"doi\":\"10.1080/19942060.2024.2399672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Researchers have long been working on interpreting and predicting the dynamics of complex systems in various fields. Conventional methods including full-scale simulations and reduced-order models a...\",\"PeriodicalId\":50524,\"journal\":{\"name\":\"Engineering Applications of Computational Fluid Mechanics\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-09-19\",\"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/10.1080/19942060.2024.2399672\",\"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/10.1080/19942060.2024.2399672","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}
Neural downscaling for complex systems: from large-scale to small-scale by neural operator
Researchers have long been working on interpreting and predicting the dynamics of complex systems in various fields. Conventional methods including full-scale simulations and reduced-order models a...
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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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