{"title":"求解二维Navier-Stokes方程的双时间尺度CNN","authors":"T. Kozek, T. Roska","doi":"10.1109/CNNA.1994.381668","DOIUrl":null,"url":null,"abstract":"A practical cellular neural network (CNN) approximation to the Navier Stokes equation describing viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite difference discretization scheme, including the double time-scale CNN dynamics and the treatment of various types of boundary conditions are explained. The operation of the continuous time model is demonstrated through several examples.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"A double time-scale CNN for solving 2-D Navier-Stokes equations\",\"authors\":\"T. Kozek, T. Roska\",\"doi\":\"10.1109/CNNA.1994.381668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A practical cellular neural network (CNN) approximation to the Navier Stokes equation describing viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite difference discretization scheme, including the double time-scale CNN dynamics and the treatment of various types of boundary conditions are explained. The operation of the continuous time model is demonstrated through several examples.<<ETX>>\",\"PeriodicalId\":248898,\"journal\":{\"name\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1994.381668\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1994.381668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A double time-scale CNN for solving 2-D Navier-Stokes equations
A practical cellular neural network (CNN) approximation to the Navier Stokes equation describing viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite difference discretization scheme, including the double time-scale CNN dynamics and the treatment of various types of boundary conditions are explained. The operation of the continuous time model is demonstrated through several examples.<>
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