Global optimization for data assimilation in landslide tsunami models
![Thumbnail](/dspace/bitstream/handle/2183/38174/FerreiroFerreiro_AnaMaria_2020_Global_optimization_tsunamis_models.pdf.jpg?sequence=5&isAllowed=y)
Use este enlace para citar
http://hdl.handle.net/2183/38174
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 3.0 España
Colecciones
- GI-M2NICA - Artigos [72]
Metadatos
Mostrar el registro completo del ítemTítulo
Global optimization for data assimilation in landslide tsunami modelsAutor(es)
Fecha
2020-02-15Resumen
[Abstract]: The goal of this article is to make automatic data assimilation for a landslide tsunami model, given by the coupling between a non-hydrostatic multi-layer shallow-water and a Savage-Hutter granular landslide model for submarine avalanches. The coupled model is discretized using a positivity preserving second-order path-conservative finite volume scheme. Then, the data assimilation problem is posed in a global optimization framework. Later, multi-path parallel metaheuristic stochastic global optimization algorithms are developed. More precisely, a multi-path Simulated Annealing algorithm is compared with a multi-path hybrid global optimization algorithm based on coupling Simulated Annealing with gradient local searchers.
Palabras clave
Tsunamis
Submarine avalanches
Finite volume methods
Data assimilation
Global optimization
Parallel computing
Submarine avalanches
Finite volume methods
Data assimilation
Global optimization
Parallel computing
Descripción
© 2020. This manuscript version is made available under the CC-BY-NCND
4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.
This version of the article has been accepted for publication in Journal of
Computational Physics (1090-2716). The Version of Record is available
online at 10.1016/j.jcp.2019.109069.
Versión del editor
Derechos
Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1090-2716
0021-9991
0021-9991