Discovering Differential Equations from Earth Observation Data

Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understand...

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Bibliographic Details
Main Authors: Adsuara, Jose E., Perez-Suay, Adrian, Moreno-Martinez, Alvaro, Camps-Valls, Gustau, Kraemer, Guido, Reichstein, Markus, Mahecha, Miguel
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Biosphere
Data models
Earth
Ecosystems
Interpretability
Libraries
Machine learning
Mathematical model
ODE models
Ordinary differential equation
Sparse regression
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Summary:Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understanding the governing equations of the system from observational data seems an elusive problem. In this paper we introduce sparse regression to uncover a set of governing equations in the form of a system of ordinary differential equations (ODEs). The presented method is used to explicitly describe variable relations by identifying the most expressive and simplest ODEs explaining data to model relevant components of the biosphere.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9324639