diffEcosys¶

Alt text

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.

Code Release¶

github version: https://github.com/hydroPKDN/diffEcosys/

zenodo version: https://zenodo.org/records/8067204

Bibtex Citation¶

@article{Aboelyazeed2023,
  doi = {10.5194/bg-20-2671-2023},
  url = {https://doi.org/10.5194/bg-20-2671-2023},
  year = {2023},
  month = jul,
  publisher = {Copernicus {GmbH}},
  volume = {20},
  number = {13},
  pages = {2671--2692},
  author = {Doaa Aboelyazeed and Chonggang Xu and Forrest M. Hoffman and Jiangtao Liu and Alex W. Jones and Chris Rackauckas and Kathryn Lawson and Chaopeng Shen},
  title = {A differentiable,  physics-informed ecosystem modeling and  learning framework for large-scale inverse problems:  demonstration with photosynthesis simulations},
  journal = {Biogeosciences}
}