Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 22
Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 22
Development and technical paper
 | Highlight paper
 | 
16 Nov 2023
Development and technical paper | Highlight paper |  | 16 Nov 2023

Universal differential equations for glacier ice flow modelling

Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez

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Latest update: 13 Dec 2024
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Executive editor
The integration of neural networks into PDE solvers to simulate systems for which the PDE models are incomplete is a key advance at the cutting edge of geoscientific modelling. The approach presented here is applicable far beyond the realm of ice modelling, and will be of interest to model developers and users across geoscience and beyond.
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Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
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