Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results
The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonl...
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Copernicus Publications
2005-01-01
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Series: | Nonlinear Processes in Geophysics |
Online Access: | http://www.nonlin-processes-geophys.net/12/505/2005/npg-12-505-2005.pdf |
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doaj-9145c0dd50a74cfb87ffffb249c7883b2020-11-24T22:04:52ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462005-01-01124505513Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary resultsC. Del NegroL. FortunaA. VicariA. VicariThe forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs). The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a lattice-like structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves) in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow.http://www.nonlin-processes-geophys.net/12/505/2005/npg-12-505-2005.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. Del Negro L. Fortuna A. Vicari A. Vicari |
spellingShingle |
C. Del Negro L. Fortuna A. Vicari A. Vicari Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results Nonlinear Processes in Geophysics |
author_facet |
C. Del Negro L. Fortuna A. Vicari A. Vicari |
author_sort |
C. Del Negro |
title |
Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results |
title_short |
Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results |
title_full |
Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results |
title_fullStr |
Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results |
title_full_unstemmed |
Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results |
title_sort |
modelling lava flows by cellular nonlinear networks (cnn): preliminary results |
publisher |
Copernicus Publications |
series |
Nonlinear Processes in Geophysics |
issn |
1023-5809 1607-7946 |
publishDate |
2005-01-01 |
description |
The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs). The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a lattice-like structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves) in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow. |
url |
http://www.nonlin-processes-geophys.net/12/505/2005/npg-12-505-2005.pdf |
work_keys_str_mv |
AT cdelnegro modellinglavaflowsbycellularnonlinearnetworkscnnpreliminaryresults AT lfortuna modellinglavaflowsbycellularnonlinearnetworkscnnpreliminaryresults AT avicari modellinglavaflowsbycellularnonlinearnetworkscnnpreliminaryresults AT avicari modellinglavaflowsbycellularnonlinearnetworkscnnpreliminaryresults |
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