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A DOUBLE TIME—SCALE CNN FOR SOLVING TWO‐DIMENSIONAL NAVIER—STOKES EQUATIONS
Author(s) -
KOZEK T.,
ROSKA T.
Publication year - 1996
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/(sici)1097-007x(199601/02)24:1<49::aid-cta903>3.0.co;2-i
Subject(s) - discretization , navier–stokes equations , cellular neural network , compressibility , flow (mathematics) , hagen–poiseuille flow from the navier–stokes equations , computer science , scheme (mathematics) , stokes flow , boundary (topology) , scale (ratio) , mathematics , artificial neural network , mathematical analysis , artificial intelligence , geometry , physics , mechanics , quantum mechanics
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.