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Arbitrary-Scale Downscaling of Tidal Current Data Using Implicit Continuous Representation
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DOI：10.1109/ACCESS.2024.3478782.png)
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Abstract
En 中文
Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, their computational cost hinders generating data of varying resolutions. As an alternative, deep learning-based downscaling methods have gained traction due to their faster inference speeds. However, most of them are limited to only inference fixed scale and overlook important characteristics of target geoscientific data. In this paper, we propose a novel downscaling framework for tidal current data, addressing its unique characteristics, which are dissimilar to images: heterogeneity and local dependency. Moreover, our framework can generate any arbitrary-scale output utilizing a continuous representation model. Our proposed framework demonstrates significantly improved flow velocity predictions by 93.21% (MSE) and 63.85% (MAE) compared to the LIIF while achieving a remarkable 33.2% reduction in FLOPs. The code will be available on GitHub: https://github.com/dslisleedh/LIIFNM.
Keywords:
Feature extraction
Numerical models
Frequency modulation
Superresolution
Data models
Computational efficiency
Costs
Training
Predictive models
Data mining
Tidal energy
Ocean circulation
Continuous time systems
Arbitrary-scale downscaling
image super-resolution
implicit neural representation
oceanic tidal current data
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W