Double time-scale CNN for solving 2-D Navier-Stokes equations

T. Kozek, T. Roska

Research output: Contribution to conferencePaper

8 Citations (Scopus)

Abstract

A practical cellular neural network (CNN) approximation to the Navier-Stokes equation describing viscous flow of incompressible fluids is presented here. 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.

Original languageEnglish
Pages267-272
Number of pages6
Publication statusPublished - Dec 1 1994
EventProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) - Rome, Italy
Duration: Dec 18 1994Dec 21 1994

Other

OtherProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)
CityRome, Italy
Period12/18/9412/21/94

ASJC Scopus subject areas

  • Software

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    Kozek, T., & Roska, T. (1994). Double time-scale CNN for solving 2-D Navier-Stokes equations. 267-272. Paper presented at Proceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94), Rome, Italy, .