Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 16
Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5311-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5311-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 16
Model description paper
 | 
26 Aug 2025
Model description paper |  | 26 Aug 2025

A Python library for solving ice sheet modeling problems using physics-informed neural networks, PINNICLE v1.0

Gong Cheng, Mansa Krishna, and Mathieu Morlighem

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Short summary
Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE (Physics-Informed Neural Networks for Ice and CLimatE), an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
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