Articles | Volume 20, issue 13
https://doi.org/10.5194/bg-20-2671-2023
https://doi.org/10.5194/bg-20-2671-2023
Research article
 | 
06 Jul 2023
Research article |  | 06 Jul 2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-211', Anonymous Referee #1, 14 Dec 2022
    • AC2: 'Reply on RC1', Chaopeng Shen, 26 Jan 2023
      • AC4: 'Reply on AC2', Chaopeng Shen, 31 Jan 2023
  • RC2: 'Comment on bg-2022-211', Anonymous Referee #2, 23 Jan 2023
    • AC1: 'Reply on RC2', Chaopeng Shen, 23 Jan 2023
    • AC3: 'Reply on RC2', Chaopeng Shen, 31 Jan 2023
      • RC3: 'Reply on AC3', Anonymous Referee #2, 01 Feb 2023
        • AC5: 'Reply on RC3', Chaopeng Shen, 01 Feb 2023
        • AC6: 'Reply on RC3', Chaopeng Shen, 08 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (13 Feb 2023) by Andreas Ibrom
AR by Chaopeng Shen on behalf of the Authors (17 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Reconsider after major revisions (20 Mar 2023) by Andreas Ibrom
ED: Referee Nomination & Report Request started (23 Mar 2023) by Andreas Ibrom
RR by Anonymous Referee #2 (06 Apr 2023)
ED: Publish subject to technical corrections (10 May 2023) by Andreas Ibrom
AR by Chaopeng Shen on behalf of the Authors (27 May 2023)  Author's response   Manuscript 
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Short summary
Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
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