Global optimization for data assimilation in landslide tsunamis models
<|reference_start|>Global optimization for data assimilation in landslide tsunamis models: 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.<|reference_end|>
@article{ferreiro-ferreiro2024global,
    title={Global optimization for data assimilation in landslide tsunamis models},
    author={A.M. Ferreiro-Ferreiro, J.A. Garc'ia-Rodr'iguez, J.G. L'opez-Salas,
  C. Escalante, and M.J. Castro},
    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},
    year={2024},
    doi={10.1016/j.jcp.2019.109069},
    archivePrefix={arXiv},
    eprint={2408.11819},
    primaryClass={physics.geo-ph cs.NA math.NA}
}