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.

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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

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article

Towards data-driven discovery of governing equations in geosciences

Wenxiang Song, Harry Vereecken, Gustau Camps‐Valls, Mathew Williams et al.
13 citations
Communications Earth & Environment
Reservoir Engineering and Simulation Methods
4.37
article

Towards data-driven discovery of governing equations in geosciences

Wenxiang Song, Harry Vereecken, Gustau Camps‐Valls, Mathew Williams, Shijie Jiang, Liangsheng Shi, Markus Reichstein, Leilei He, Lu Zhang, Xiaolong Hu
article en
13 citations

Abstract

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.

Communications Earth & EnvironmentVol. 5(1)
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)
Sight Research UK, National Natural Science Foundation of China, Natural Environment Research Council
Climate action
Openalex Percentile: Top 5%
Reservoir Engineering and Simulation Methods
4.37
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