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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10649 |
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| _version_ | 1866916362889199616 |
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| author | Horuz, Coşku Can Karlbauer, Matthias Praditia, Timothy Oladyshkin, Sergey Nowak, Wolfgang Otte, Sebastian |
| author_facet | Horuz, Coşku Can Karlbauer, Matthias Praditia, Timothy Oladyshkin, Sergey Nowak, Wolfgang Otte, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inferring Underwater Topography with FINN Horuz, Coşku Can Karlbauer, Matthias Praditia, Timothy Oladyshkin, Sergey Nowak, Wolfgang Otte, Sebastian Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Computational Physics Fluid Dynamics 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. |
| title | Inferring Underwater Topography with FINN |
| topic | Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Computational Physics Fluid Dynamics |
| url | https://arxiv.org/abs/2408.10649 |