Articles | Volume 20, issue 13
https://doi.org/10.5194/bg-20-2671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2023
Research article |
| 06 Jul 2023
| 06 Jul 2023
A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations
Doaa Aboelyazeed
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Chonggang Xu
Earth and Environmental Sciences Division, Los Alamos National
Laboratory, Los Alamos, NM 87544, USA
Forrest M. Hoffman
Computational Sciences & Engineering Division and the Climate
Change Science Institute, Oak Ridge National Laboratory, Oak Ridge,
TN 37830, USA
Department of Civil and Environmental Engineering, University of
Tennessee, Knoxville, TN 37996, USA
Jiangtao Liu
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Alex W. Jones
SciML, Open Source Software Organization, Cambridge, MA, USA
Chris Rackauckas
Computer Science and Artificial Intelligence Laboratory (CSAIL),
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Kathryn Lawson
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
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Cited
13 citations as recorded by crossref.
- The intricacies of vegetation responses to changing moisture conditions J. Green 10.1111/nph.20182
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
- Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. 10.1016/j.agrformet.2024.110264
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
- Plant science in the age of simulation intelligence M. Stock et al. 10.3389/fpls.2023.1299208
- Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
- A Surrogate Model for Shallow Water Equations Solvers with Deep Learning Y. Song et al. 10.1061/JHEND8.HYENG-13190
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
- Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats J. Liu et al. 10.5194/gmd-16-1553-2023
11 citations as recorded by crossref.
- The intricacies of vegetation responses to changing moisture conditions J. Green 10.1111/nph.20182
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
- Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models W. Yuan et al. 10.1016/j.agrformet.2024.110264
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
- Plant science in the age of simulation intelligence M. Stock et al. 10.3389/fpls.2023.1299208
- Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
- A Surrogate Model for Shallow Water Equations Solvers with Deep Learning Y. Song et al. 10.1061/JHEND8.HYENG-13190
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
2 citations as recorded by crossref.
Latest update: 01 Apr 2025
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.
Photosynthesis is critical for life and has been affected by the changing climate. Many...
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