DocumentCode
465047
Title
Models of Lava Flow Through the CNN-Based E^3 Architecture
Author
Arena, P. ; Buscemi, G. ; Carambia, B. ; Del Negro, C. ; Fortuna, L. ; Frasca, M. ; Vicari, A.
Author_Institution
Dipt. di Ingegneria Elettrica, Elettronica e dei Sistemi, Universita degli Studi di Catania
fYear
2007
fDate
27-30 May 2007
Firstpage
2914
Lastpage
2917
Abstract
Many works investigated the phenomenon of lava flow through numerical models, obtaining excellent results, although most of the models require several approximations. Each model, in fact, has its restrictions: for instance, some of them work only on inclined planes, while others do not consider cooling processes associated to lava flow and so on. Simplifications are often needed to afford the computational effort required by the problem. Although the increasing computational capability of computers, due to technological progress, can be very useful to quickly resolve differential equations, which are essential to study lava flows, it is still important to take into account alternative solutions to the problem. One of these solutions is the use of parallel analog processors, namely cellular nonlinear networks (CNNs). In particular, the approach used in this work is based on the E^3 architecture (Caponetto et al., 2004) used for the first time to study lava flows. Two different models are proposed.
Keywords
cellular neural nets; differential equations; geophysics computing; CNN-based E^3 architecture; cellular nonlinear networks; cooling process; differential equations; lava flow; numerical models; parallel analog processors; Cellular neural networks; Computational modeling; Computer architecture; Cooling; Differential equations; Emergent phenomena; Numerical models; Parallel algorithms; Parallel processing; Surfaces;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on
Conference_Location
New Orleans, LA
Print_ISBN
1-4244-0920-9
Electronic_ISBN
1-4244-0921-7
Type
conf
DOI
10.1109/ISCAS.2007.377859
Filename
4253288
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