1. Experimental Validation: Direct comparison of model predictions with experimental data. 2. Cross-validation: Dividing data into subsets, using some for calibration and others for validation. 3. Benchmarking: Comparing the model’s performance with established models or standards. 4. Peer Review: Subjecting the model and its predictions to scrutiny by the scientific community.
