What does verification in simulation entail?

What does verification in simulation entail?

• A. Determining that the simulation calculations are logically correct.
• B. Ensuring the model matches a real system.
• C. Developing the model free of errors.
• D. Validating against data.

Answer: (A)

Verification in simulation is about building the model right: it checks that the simulation calculations are implemented correctly and driven by the intended logic. In practice this means confirming that the code, algorithms, and numerical methods actually solve the model as designed. For Monte Carlo, you verify things like the correctness of equations, the way randomness is used, and the reproducibility of results under controlled conditions. You might run unit tests, check invariants, test with simple solvable cases, ensure numerical tolerances are appropriate, and use known benchmarks or analytical results to spot implementation errors. This focus on internal correctness distinguishes verification from validation, which is about whether the model outputs align with real-world data or systems. The other choices describe validity against reality or broader model quality, not the internal logical correctness of the calculations, which is why the direct aim of verification is to ensure the simulation calculations are logically correct.

Verification in simulation is about building the model right: it checks that the simulation calculations are implemented correctly and driven by the intended logic. In practice this means confirming that the code, algorithms, and numerical methods actually solve the model as designed. For Monte Carlo, you verify things like the correctness of equations, the way randomness is used, and the reproducibility of results under controlled conditions. You might run unit tests, check invariants, test with simple solvable cases, ensure numerical tolerances are appropriate, and use known benchmarks or analytical results to spot implementation errors. This focus on internal correctness distinguishes verification from validation, which is about whether the model outputs align with real-world data or systems. The other choices describe validity against reality or broader model quality, not the internal logical correctness of the calculations, which is why the direct aim of verification is to ensure the simulation calculations are logically correct.