Latent assimilation with implicit neural representations for unknown dynamics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929286296895488 |
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| author | Li, Zhuoyuan Dong, Bin Zhang, Pingwen |
| author_facet | Li, Zhuoyuan Dong, Bin Zhang, Pingwen |
| contents | Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding of underlying mechanisms. To address these challenges, this study presents a novel assimilation framework, termed Latent Assimilation with Implicit Neural Representations (LAINR). By introducing Spherical Implicit Neural Representations (SINR) along with a data-driven uncertainty estimator of the trained neural networks, LAINR enhances efficiency in assimilation process. Experimental results indicate that LAINR holds certain advantage over existing methods based on AutoEncoders, both in terms of accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09574 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Latent assimilation with implicit neural representations for unknown dynamics Li, Zhuoyuan Dong, Bin Zhang, Pingwen Machine Learning Mathematical Physics Optimization and Control Atmospheric and Oceanic Physics 68T07, 49N45, 33C55 Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding of underlying mechanisms. To address these challenges, this study presents a novel assimilation framework, termed Latent Assimilation with Implicit Neural Representations (LAINR). By introducing Spherical Implicit Neural Representations (SINR) along with a data-driven uncertainty estimator of the trained neural networks, LAINR enhances efficiency in assimilation process. Experimental results indicate that LAINR holds certain advantage over existing methods based on AutoEncoders, both in terms of accuracy and efficiency. |
| title | Latent assimilation with implicit neural representations for unknown dynamics |
| topic | Machine Learning Mathematical Physics Optimization and Control Atmospheric and Oceanic Physics 68T07, 49N45, 33C55 |
| url | https://arxiv.org/abs/2309.09574 |