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The Innovation Informatics > 2026 Vol. 2 > No. 1 > 100031
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Physics-informed representation learning for a digital-ocean twin: The AlphaOcean framework

  • 1.

    Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

  • 2.

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China

  • 3.

    IMAS-Hobart, University of Tasmania, Hobart, Tasmania 7004, Australia

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  • Corresponding author: sufz@lreis.ac.cn
  • Received Date:  January 09, 2026
    Accepted Date:  January 27, 2026
    Published Online:  February 09, 2026
The Innovation Informatics  2(1),  https://doi.org/10.59717/j.xinn-inform.2026.100031  |  Cite this article
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    • REFERENCES
  • [1] Hoegh-Guldberg O., Northrop E. and Lubchenco J. (2019). The ocean is key to achieving climate and societal goals. Science 365:1372-1374. DOI:10.1126/science.aaz43

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    [2] Révelard A., Tintoré J., Verron J., et al. (2022). Ocean integration: The needs and challenges of effective coordination within the ocean observing system. Front. Mar. Sci. 8:737671. DOI:10.3389/fmars.2021.737671

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    [4] Tollefson J. (2025). Google AI model creates maps of Earth ‘at any place and time’. Nature 644:313. DOI: 10.1038/d41586-025-02412-1

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  • Cite this article:

    Yan F., Fan R., Lyne V., et al. (2026). Physics-informed representation learning for a digital-ocean twin: The AlphaOcean framework. The Innovation Informatics 2:100031. https://doi.org/10.59717/j.xinn-inform.2026.100031
    Yan F., Fan R., Lyne V., et al. (2026). Physics-informed representation learning for a digital-ocean twin: The AlphaOcean framework. The Innovation Informatics 2:100031. https://doi.org/10.59717/j.xinn-inform.2026.100031

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