求解二维Navier-Stokes方程的双时间尺度CNN

Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI:10.1109/CNNA.1994.381668

T. Kozek, T. Roska

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引用次数: 15

摘要

提出了一种实用的细胞神经网络(CNN)逼近描述不可压缩流体粘性流动的Navier Stokes方程。说明了基于有限差分离散化方案的CNN模板的实现，包括双时间尺度CNN动态和各种类型边界条件的处理。通过几个算例说明了连续时间模型的操作。

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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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Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)

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