A double time-scale CNN for solving two-dimensional Navier-Stokes equations

T. Kozek, T. Roska

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

A practical cellular neural network (CNN) approximation to the Navier-Stokes equation describing the viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite-difference discretization scheme, including the double-timescale 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
Pages (from-to)49-55
Number of pages7
JournalInternational Journal of Circuit Theory and Applications
Volume24
Issue number1
Publication statusPublished - Jan 1996

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Cellular neural networks
Cellular Networks
Navier Stokes equations
Navier-Stokes Equations
Time Scales
Neural Networks
Network Dynamics
Continuous-time Model
Discretization Scheme
Viscous flow
Viscous Flow
Difference Scheme
Incompressible Fluid
Template
Finite Difference
Boundary conditions
Fluids
Approximation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

A double time-scale CNN for solving two-dimensional Navier-Stokes equations. / Kozek, T.; Roska, T.

In: International Journal of Circuit Theory and Applications, Vol. 24, No. 1, 01.1996, p. 49-55.

Research output: Contribution to journalArticle

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