Ferreiro Ferreiro, Ana MaríaGarcía Rodríguez, José AntonioLópez-Salas, José GermánEscalante Sánchez, CiprianoCastro Díaz, Manuel Jesús2024-07-192024-07-192020-02-15Ferreiro-Ferreiro, A. M., García-Rodríguez, J. A., López-Salas, J. G., Escalante, C., & Castro, M. J. (2020). Global optimization for data assimilation in landslide tsunami models. Journal of Computational Physics, 403, 109069. https://doi.org/10.1016/j.jcp.2019.1090691090-27160021-9991http://hdl.handle.net/2183/38174© 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.[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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/TsunamisSubmarine avalanchesFinite volume methodsData assimilationGlobal optimizationParallel computingjournal articleopen access10.1016/j.jcp.2019.109069