Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance.
Any inference of stochastic variance estimated by an agent-based model using parameters optimized by our ODE model are thus somewhat troublesome.
Rate constants for DOC oxidation and carbonate mineral precipitation were determined, and other model parameters were optimized using the nonlinear optimization program PEST by means of matching hydrochemical observations closely (pH, DIC, DOC, Na, K, Ca, Mg, NH4, Fe(II), SO4, Cl, CH4, saturation index of calcite and siderite).
The values of the model parameters were optimized by grid search and 5-fold cross validation (CV) [52].
Model parameters were optimized using a genetic algorithm (GA) in order to conform to the deformation observed from 2009 to 2014 and 2006 to 2009.
The partial difference equations in the model are solved by implicit finite difference method of Crank Nicolson form, and the model parameters are optimized by simplex algorithm.
Model parameters are optimized so that the model can, at least, re-produce with minimum error the data that were used in obtaining the membership functions and rules.
The model parameters were optimized through an inverse finite element (FE) procedure in order to describe both sets of experimental data.
Thereby, the model parameters are optimized by means of an inverse parameter identification strategy which, overall, leads to a very good agreement between experimentally observed and computationally predicted data.
The Drosophila melanogaster gap gene system, in which model parameters are optimized against in situ immunofluorescence intensities, demonstrates the importance of error quantification, which is applicable to a wide array of developmental modeling studies.
