Skip to main content
SHARE
Publication

Enhancing Photosynthesis Simulation Performance in ESMs with Machine Learning-Assisted Solvers

by Elias C Massoud, Nathaniel O Collier, Bharat D Sharma, Jitendra Kumar, Forrest M Hoffman
Publication Type
Conference Paper
Book Title
2024 IEEE International Conference on Big Data (BigData)
Publication Date
Page Numbers
4351 to 4356
Volume
1
Publisher Location
New Jersey, United States of America
Conference Name
2024 IEEE International Conference on Big Data (IEEE BigData 2024)
Conference Location
Washington DC, District of Columbia, United States of America
Conference Sponsor
Institute of Electrical and Electronics Engineers
Conference Date
-

When simulating vegetation dynamics, photosynthesis accounts for a large fraction of the computational cost in most Earth System Models (ESMs). This is largely since photosynthesis is represented as a system of nonlinear equations, and the solution requires the use of an initial guess followed by many iterations of the numerical solver to obtain a solution. We use machine learning (ML) to replicate the response surface of the model’s numerical solver to improve the choice of initial guess, therefore requiring fewer iterations to obtain a final solution. We implemented this test on the leaf-level calculations as well as at the canopy scale, and for both we observed fewer iterations of the photosynthesis solver when a ML-based initial guess was implemented. The model tested here is the Energy Exascale Earth System Model - Land Model (ELM). The ML-based algorithms used here are trained on simulations from the model itself and used only to improve the initial guess for the solver; therefore, the model maintains its own set of physics to obtain the final solution. This work shows novel ways to utilize ML-based methods to improve the performance of numerical solvers in ESMs.