Advanced Search
Article Contents
ABOUT THIS ARTICLE
The Innovation Informatics > Online
COMMENTARY   Open Access     Cite

Physics-informed neural networks help to strongly reduce uncertainty in ocean state forecasting

  • 1.

    Shandong Provincial Key Laboratory of Marine Engineering Geology and the Environment, Ocean University of China, Qingdao 266100, China

  • 2.

    Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China

  • 3.

    College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China

  • 4.

    Ceyear Technologies Co., Ltd., Qingdao 266555, China

  • 5.

    School of Mathematics and Physics, The University of Queensland, St Lucia 4072, Australia

More Information
  • Corresponding author: zhuangcaitian@ouc.edu.cn 
  • Received Date:  April 14, 2026
    Accepted Date:  June 04, 2026
    Published Online:  June 08, 2026
The Innovation Informatics  https://doi.org/10.59717/j.xinn-inform.2026.100051  |  Cite this article
    • FullText(HTML)
  • 加载中
    • REFERENCES
  • [1] Zhao T., Wang S., Ouyang C., et al. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. The Innovation 5:100691. DOI:10.1016/j.xinn.2024.100691

    View in Article CrossRef Google Scholar

    [2] Dong S., Li Y., Yin X., et al. (2026). Physics-guided deep learning for global sea surface temperature forecasting: Balancing accuracy and stability across timescales. Geosci. Front. 17:102255. DOI:10.1016/j.gsf.2026.102255

    View in Article CrossRef Google Scholar

    [3] El Aouni A., Gaudel Q., Regnier C., et al. (2025). GLONET: Mercator’s end-to-end neural global ocean forecasting system. J. Geophys. Res. Mach. Learn. Comput. 2:e2025JH000686. DOI:10.1029/2025JH000686

    View in Article CrossRef Google Scholar

    [4] Guo Z., Lyu P., Ling F., et al. (2025). Data-driven global ocean modeling for seasonal to decadal prediction. Sci. Adv. 11:eadu2488. DOI: 10.1126/sciadv. adu2488. DOI:10.1126/sciadv.adu2488

    View in Article CrossRef Google Scholar

    [5] Li X., Liu B., Zheng G., et al. (2020). Deep-learning-based information mining from ocean remote-sensing imagery. Natl. Sci. Rev. 7:1584−1605. DOI:10.1093/nsr/nwaa047

    View in Article CrossRef Google Scholar

    • Cited by

  • Cite this article:

    Chen T., Tian Z., Li X., et al. (2026). Physics-informed neural networks help to strongly reduce uncertainty in ocean state forecasting. The Innovation Informatics 2:100051. https://doi.org/10.59717/j.xinn-inform.2026.100051
    Chen T., Tian Z., Li X., et al. (2026). Physics-informed neural networks help to strongly reduce uncertainty in ocean state forecasting. The Innovation Informatics 2:100051. https://doi.org/10.59717/j.xinn-inform.2026.100051

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

PDF
Figures(1)    

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(880) PDF downloads(347)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return

    {{newsColumn.name}}

    {{newsColumn.name}}

    Stay connected

    {{newsColumn.name}}

    Website copyright © Innovation Press. Published articles are licensed under CC BY with copyright retained by the authors.