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Global optimization for data assimilation in landslide tsunamis models
- Source :
- A.M. Ferreiro-Ferreiro, J.A. Garc\'ia-Rodr\'iguez, J.G. L\'opez-Salas, C. Escalante, M.J. Castro, Global optimization for data assimilation in landslide tsunami models, Journal of Computational Physics, Volume 403, 2020, 109069
- Publication Year :
- 2024
-
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. The data assimilation problem is posed in a global optimization framework and we develop and compare parallel metaheuristic stochastic global optimization algorithms, more precisely multi-path versions of the Simulated Annealing algorithm, with hybrid global optimization algorithms based on hybridizing Simulated Annealing with gradient local searchers, like L-BGFS-B.
- Subjects :
- Physics - Geophysics
Mathematics - Numerical Analysis
Subjects
Details
- Database :
- arXiv
- Journal :
- A.M. Ferreiro-Ferreiro, J.A. Garc\'ia-Rodr\'iguez, J.G. L\'opez-Salas, C. Escalante, M.J. Castro, Global optimization for data assimilation in landslide tsunami models, Journal of Computational Physics, Volume 403, 2020, 109069
- Publication Type :
- Report
- Accession number :
- edsarx.2408.11819
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.jcp.2019.109069