Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5311-2025
https://doi.org/10.5194/gmd-18-5311-2025
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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Cited articles

Aschwanden, A., Fahnestock, M. A., Truffer, M., Brinkerhoff, D. J., Hock, R., Khroulev, K., Mottram, R., and Khan, S. A.: Contribution of the Greenland Ice Sheet to sea level over the next millennium, Sci. Adv., 5, eaav9396, https://doi.org/10.1126/sciadv.aav9396, 2019. a
Aschwanden, A., Bartholomaus, T. C., Brinkerhoff, D. J., and Truffer, M.: Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level, The Cryosphere, 15, 5705–5715, https://doi.org/10.5194/tc-15-5705-2021, 2021. a
Bauer, P., Thorpe, A., and Brunet, G.: The Quiet Revolution of Numerical Weather Prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bolibar, J., Sapienza, F., Maussion, F., Lguensat, R., Wouters, B., and Pérez, F.: Universal differential equations for glacier ice flow modelling, Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, 2023. a, b
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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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