Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations

doi:doi:10.57702/0125i72t

Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations

Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting.

BibTex:

@dataset{Jaesung_Park_and_Sungchul_Hong_and_Yoonseo_Cho_and_Jong-June_Jeon_2024,
    abstract = {Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting.},
    author = {Jaesung Park and Sungchul Hong and Yoonseo Cho and Jong-June Jeon},
    doi = {10.57702/0125i72t},
    institution = {No Organization},
    keyword = {'Convolutional Neural Networks', 'Forecasting', 'Ordinary Differential Equations', 'Sea Ice'},
    month = {dec},
    publisher = {TIB},
    title = {Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations},
    url = {https://service.tib.eu/ldmservice/dataset/unicorn--u-net-for-sea-ice-forecasting-with-convolutional-neural-ordinary-differential-equations},
    year = {2024}
}