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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2025-1188 - No compliance with the policy of the journal', Juan Antonio Añel, 09 Apr 2025
    • AC1: 'Reply on CEC1', Gong Cheng, 09 Apr 2025
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 09 Apr 2025
  • RC1: 'Comment on egusphere-2025-1188', Anonymous Referee #1, 20 May 2025
  • RC2: 'Comment on egusphere-2025-1188', Facundo Sapienza, 25 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Gong Cheng on behalf of the Authors (01 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (10 Jun 2025) by Ludovic Räss
AR by Gong Cheng on behalf of the Authors (10 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (11 Jun 2025) by Ludovic Räss
AR by Gong Cheng on behalf of the Authors (11 Jun 2025)  Manuscript 
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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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