Bathymetry reconstruction from experimental data using PDE-constrained optimisation
<|reference_start|>Bathymetry reconstruction from experimental data using PDE-constrained optimisation: Knowledge of the bottom topography, also called bathymetry, of rivers, seas or the ocean is important for many areas of maritime science and civil engineering. While direct measurements are possible, they are time consuming and expensive. Therefore, many approaches have been proposed how to infer the bathymetry from measurements of surface waves. Mathematically, this is an inverse problem where an unknown system state needs to be reconstructed from observations with a suitable model for the flow as constraint. In many cases, the shallow water equations can be used to describe the flow. While theoretical studies of the efficacy of such a PDE-constrained optimisation approach for bathymetry reconstruction exist, there seem to be few publications that study its application to data obtained from real-world measurements. This paper shows that the approach can, at least qualitatively, reconstruct a Gaussian-shaped bathymetry in a wave flume from measurements of the water height at up to three points. Achieved normalized root mean square errors (NRMSE) are in line with other approaches.<|reference_end|>
@article{angel2024bathymetry,
    title={Bathymetry reconstruction from experimental data using PDE-constrained
  optimisation},
    author={Judith Angel, J"orn Behrens, Sebastian G"otschel, Marten Hollm,
  Daniel Ruprecht, Robert Seifried},
    journal={Computers & Fluids 278, pp. 106321, 2024},
    year={2024},
    doi={10.1016/j.compfluid.2024.106321},
    archivePrefix={arXiv},
    eprint={2404.05556},
    primaryClass={math.NA cs.CE cs.NA}
}