Towards data-driven discovery of governing equations in geosciences
Governing equations are foundations for modelling, predicting, and understanding the Earth system. The Earth system is undergoing rapid change, and the conventional approaches for establishing governing equations, such as empirical generalisations, are becoming increasingly challenging to deal with the complexity and diversity of the geoscience processes we study today. In this Perspective, we explore data-driven equation discovery, a novel scientific artificial intelligence pathway, for advancing geosciences. Data-driven equation discovery identifies hidden patterns from data and transforms them into interpretable equation representations, automating and accelerating equation discovery processes. It provides a practical approach for geoscientists to model and understand complex geoscience processes based on big Earth data. The final vision is to uncover new clear, describable, and quantifiable equations in various geoscience disciplines. We summarize opportunities and highlight that challenges in this field should be addressed by interdisciplinary collaborations. Data-driven equation discovery, a new scientific artificial intelligence pathway, can identify hidden patterns in data and transform them into interpretable equations, automating and accelerating discovery processes in various geoscience disciplines, according to a review of the advantages and potential challenges of data-driven equation discovery in geoscience.
Authors
- Wenxiang Song (ORCID: https://orcid.org/0000-0002-9834-7197)
- Harry Vereecken (ORCID: https://orcid.org/0000-0002-8051-8517)
- Gustau Camps‐Valls (ORCID: https://orcid.org/0000-0003-1683-2138)
- Mathew Williams (ORCID: https://orcid.org/0000-0001-6117-5208)
- Shijie Jiang (ORCID: https://orcid.org/0000-0002-2808-9559)
- Liangsheng Shi (ORCID: https://orcid.org/0000-0003-0446-0488)
- Markus Reichstein (ORCID: https://orcid.org/0000-0001-5736-1112)
- Leilei He (ORCID: https://orcid.org/0000-0002-7715-5989)
- Lu Zhang (ORCID: https://orcid.org/0000-0002-2225-9772)
- Xiaolong Hu (ORCID: https://orcid.org/0009-0007-2015-4946)
Institutions
- Universitat de València (ES)
- Forschungszentrum Jülich (DE)
- Wuhan University (CN)
- Max Planck Institute for Biogeochemistry (DE)
- CSIRO Land and Water (AU)
- University of Edinburgh (GB)
Publication Details
- Journal
- Communications Earth & Environment
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1038/s43247-024-01760-6
- Citations
- 13
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 4.37
Funders
- Sight Research UK
- National Natural Science Foundation of China
- Natural Environment Research Council