Bayesian Optimization with Applications to LPJ-GUESS
This work applied Bayesian Optimization (BO) for the task of calibrating methane-related parameters in the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS v4.1). A Gaussian Process (GP) was used as the surrogate model within the BO framework. Additionally, other enhancements we applied to the BO framework such as the use of complexity-penalizing priors for GP hyperparameters and numerical
