How should the results of a computational simulation be interpreted?

The results of a computational simulation should be interpreted primarily by comparing them against known solutions and validating them with experimental data. This approach ensures that the simulation's outputs are consistent with established knowledge and real-world observations. It provides a means to verify the accuracy of the computational model and to check that the assumptions and parameters used in the simulation accurately reflect the real situation being modeled.

Validation against experimental data is crucial for establishing the reliability of the simulation results, as it provides a benchmark for comparison. This process can help identify any discrepancies due to modeling errors, numerical approximation, or incorrect assumptions, ultimately leading to more trustworthy and applicable results in engineering analysis.

In contrast, utilizing random sampling techniques does not directly enhance the validity of the simulation results themselves, and analyzing results in isolation ignores the benefits of contextualizing data with existing information. Applying theoretical formulas without considering real-world data can lead to inaccuracies, as these formulas may not adequately capture the complexities of real-world phenomena. Hence, comprehensive validation through known solutions and experimental data is essential for appropriate interpretation of simulation results.