Data Collection: Gathering high-quality experimental data under various conditions to compare with model predictions. Comparison: Quantitatively comparing the model's predictions with experimental results using statistical metrics such as root mean square error (RMSE) and coefficient of determination (R2). Sensitivity Analysis: Evaluating how sensitive the model's predictions are to changes in input parameters to understand the robustness of the model. Parameter Tuning: Adjusting model parameters to improve the fit between predicted and experimental results.
