Articles | Volume 20, issue 13
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


Total article views: 2,647 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,780 794 73 2,647 44 50
  • HTML: 1,780
  • PDF: 794
  • XML: 73
  • Total: 2,647
  • BibTeX: 44
  • EndNote: 50
Views and downloads (calculated since 02 Nov 2022)
Cumulative views and downloads (calculated since 02 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,647 (including HTML, PDF, and XML) Thereof 2,628 with geography defined and 19 with unknown origin.
Country # Views %
  • 1


Latest update: 25 May 2024
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.
Final-revised paper