Validation of computational models is a critical step to ensure their reliability. This is typically done by:
1. Comparison with Experimental Data: Computational results are compared against experimental observations to check for consistency.
2. Benchmarking: Models are tested against well-established theoretical results or other computational methods known to be accurate.
3. Sensitivity Analysis: This involves varying model parameters to understand their impact on the results, ensuring that the model behaves as expected under different conditions.
