DocumentCode :
2329975
Title :
Emulated digital CNN-UM implementation of a barotropic ocean model
Author :
Nagy, Zoltán ; Szolgay, Péter
Author_Institution :
Dept. of Image Process. & Neurocomput., Veszprem Univ., Hungary
Volume :
4
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
3137
Abstract :
The solution of partial differential equations (PDE) has long been one of the most important fields of mathematics, due to the frequent occurrence of spatio-temporal dynamics in many branches of physics, engineering and other sciences. One of the most exciting area is the simulation of compressible and incompressible fluids which appears in many important applications in aerodynamics, meteorology and oceanography. On the other hand the solution of these equations requires enormous computing power. In this paper a CNN-UM simulation of ocean currents is presented. Unfortunately the non-linearity of the governing equations does not make possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution an emulated digital CNN-UM is used where the cell model of the architecture is modified to handle the non-linearity of the model.
Keywords :
cellular neural nets; computational fluid dynamics; digital signal processing chips; flow simulation; oceanography; partial differential equations; barotropic ocean model; cell model; cellular neural network universal machine; compressible fluids; emulated digital CNN-UM; incompressible fluids; ocean currents; partial differential equations; Aerodynamics; Computational modeling; Fluid dynamics; Mathematics; Meteorology; Nonlinear equations; Oceans; Partial differential equations; Physics; Power engineering and energy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Conference_Location :
Budapest
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1381176
Filename :
1381176
Link To Document :
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