@Inproceedings{Massoud_IEEEBigData_20241215,
 author		= {Elias C. Massoud and Nathan Collier and Bharat Sharma and Jitendra Kumar and Forrest M. Hoffman},
 booktitle	= {2024 IEEE International Conference on Big Data (BigData)}, 
 title		= {Enhancing Photosynthesis Simulation Performance in ESMs with Machine Learning-Assisted Solvers},
 volume		= {},
 number		= {},
 pages		= {4351--4356},
 doi		= {10.1109/BigData62323.2024.10825207},
 year		= 2024,
 month		= dec,
 day		= 15,
 keywords	= {Earth;Computational modeling;Artificial neural networks;Machine learning;Vegetation;Data models;Mathematical models;Numerical models;Computational efficiency;Photosynthesis;photosynthesis;numerical solver;machine learning;Earth System Model},
 abstract	= {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.}
}