Global optimization for data assimilation in landslide tsunami models
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http://hdl.handle.net/2183/38174
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Global optimization for data assimilation in landslide tsunami modelsAuthor(s)
Date
2020-02-15Abstract
[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.
Keywords
Tsunamis
Submarine avalanches
Finite volume methods
Data assimilation
Global optimization
Parallel computing
Submarine avalanches
Finite volume methods
Data assimilation
Global optimization
Parallel computing
Description
© 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.
Editor version
Rights
Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1090-2716
0021-9991
0021-9991