Inferring Underwater Topography with FINN
<|reference_start|>Inferring Underwater Topography with FINN: Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physics and machine learning (ML) communities, there is a growing trend towards integrating these approaches to develop hybrid architectures known as physics-aware machine learning models. Among these, the finite volume neural network (FINN) has emerged as a recent addition. FINN has proven to be particularly efficient in uncovering latent structures in data. In this study, we explore the capabilities of FINN in tackling the shallow-water equations, which simulates wave dynamics in coastal regions. Specifically, we investigate FINN's efficacy to reconstruct underwater topography based on these particular wave equations. Our findings reveal that FINN exhibits a remarkable capacity to infer topography solely from wave dynamics, distinguishing itself from both conventional ML and physics-aware ML models. Our results underscore the potential of FINN in advancing our understanding of spatiotemporal phenomena and enhancing parametrization capabilities in related domains.<|reference_end|>
@article{horuz2024inferring,
    title={Inferring Underwater Topography with FINN},
    author={Coc{s}ku Can Horuz, Matthias Karlbauer, Timothy Praditia, Sergey
  Oladyshkin, Wolfgang Nowak, Sebastian Otte},
    journal={arXiv preprint arXiv:2408.10649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10649},
    primaryClass={cs.LG cs.AI physics.ao-ph physics.comp-ph physics.flu-dyn}
}