What are the best practices for testing simulation robustness?

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Simulation robustness is the ability of a simulation model to produce consistent and reliable results under different conditions and assumptions. Testing simulation robustness is essential for ensuring the validity and credibility of your simulation outcomes, especially when they are used for decision making or policy analysis. In this article, you will learn some of the best practices for testing simulation robustness using algorithms.

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Britt Brooks

Compact Modeling Engineer at Wolfspeed

1 Sensitivity analysis

Sensitivity analysis is a technique that examines how the simulation output changes when you vary the input parameters or model assumptions. It helps you identify the most influential factors, the range of uncertainty, and the potential scenarios that might affect your simulation results. You can use different methods for sensitivity analysis, such as one-at-a-time, global, or local approaches, depending on the complexity and dimensionality of your simulation model.

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Britt Brooks

Compact Modeling Engineer at Wolfspeed
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Your simulation model will always have bounds that need to be explored. How the model behaves outside of those boundaries will be of significant interest as well to ensure that the simulation will complete properly. The simulation boundaries should not be exceeded, however, the simulator may routinely exceed these bounds during the process of finding a solution, so your model needs to be well behaved outside of the boundaries as well. Sensitivity analysis can help you see what the real boundaries of the simulation are.

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2 Uncertainty quantification

Uncertainty quantification is a technique that measures and expresses the uncertainty in the simulation output due to the uncertainty in the input parameters or model assumptions. It helps you assess the confidence and reliability of your simulation results, as well as the risk and opportunity associated with them. You can use different methods for uncertainty quantification, such as Monte Carlo, Bayesian, or interval analysis, depending on the type and level of uncertainty in your simulation model.

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3 Verification and validation

Verification and validation are techniques that check whether your simulation model is correctly implemented and accurately represents the real system or phenomenon. Verification is the process of ensuring that your simulation model is free of errors and bugs, and that it performs as expected. Validation is the process of comparing your simulation output with empirical data or expert judgment, and evaluating whether it matches the reality within an acceptable margin of error. You can use different methods for verification and validation, such as code review, debugging, testing, calibration, or comparison, depending on the availability and quality of data and information.

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Britt Brooks

Compact Modeling Engineer at Wolfspeed
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Verification and Validation are very important for model quality and simulation accuracy, but may tell you little about the simulation robustness. Simulations are almost always a trade off between accuracy and speed (both of which will effect your robustness). If you have an example simulation that has proved to be difficult and use it as part of your verification suite, you can then learn a little about the robustness. You may wish to add a wide range of testing conditions that exceed the normal verification testing to give you more confidence in the simulation. However, you cannot guarantee robustness based on simply passing a verification suite of tests.

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4 Robust optimization

Robust optimization is a technique that optimizes your simulation model under uncertainty and variability. It helps you find the best solution or policy that is robust to different scenarios and conditions, and that minimizes the worst-case or average performance loss. You can use different methods for robust optimization, such as min-max, min-max regret, or robust counterpart, depending on the objective and constraints of your simulation model.

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5 Scenario analysis

Scenario analysis is a technique that explores the possible future outcomes of your simulation model under different assumptions and events. It helps you anticipate the potential impacts and implications of your simulation results, and prepare for the opportunities and challenges that might arise. You can use different methods for scenario analysis, such as what-if, stress testing, or foresight, depending on the purpose and scope of your simulation model.

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6 Feedback and iteration

Feedback and iteration are techniques that improve your simulation model based on the results and insights from the previous techniques. They help you refine your input parameters, model assumptions, output indicators, and solution or policy alternatives, and test them again until you reach a satisfactory level of robustness. You can use different methods for feedback and iteration, such as peer review, stakeholder engagement, or adaptive learning, depending on the context and complexity of your simulation model.

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Britt Brooks

Compact Modeling Engineer at Wolfspeed
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Probably the best way to evaluate robustness of a simulation is through feedback and iteration (if possible, this should be automated). Many times the simulation shows issues, concerns, performance, risks, etc. that were not originally anticipated or even given a thought. Evaluation of what the simulation tells you and the stakeholder will determine how to proceed, either through iteration or a complete re-start of the project. Many simulations uncover a host of new items to explore which will impact the robustness and definition of the simulation project. The project focus can then be fine tuned and the simulation adjusted accordingly.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for testing simulation robustness?

1

2

3

4

5

6

7

1 Sensitivity analysis

2 Uncertainty quantification

3 Verification and validation

4 Robust optimization

5 Scenario analysis

6 Feedback and iteration

7 Here’s what else to consider

Algorithms

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What are the best practices for testing simulation robustness?

1

2

3

4

5

6

7

1 Sensitivity analysis

2 Uncertainty quantification

3 Verification and validation

4 Robust optimization

5 Scenario analysis

6 Feedback and iteration

7 Here’s what else to consider

Algorithms

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