How can you increase a simulation's accuracy?

1 Choose appropriate models

The first step to increase your simulation's accuracy is to choose appropriate models that represent the system or phenomenon you want to simulate. Models are simplified representations that capture the essential features and behaviors of the real world, but they also make assumptions and approximations that can introduce errors. You should choose models that are based on sound theory, empirical evidence, and domain knowledge, and that match the level of detail and complexity you need for your simulation objectives. You should also validate your models by comparing them with historical data, experiments, or other sources of truth.

2 Use accurate inputs

The second step to increase your simulation's accuracy is to use accurate inputs that reflect the current or expected state of the system or phenomenon you want to simulate. Inputs are the data, parameters, and variables that feed into your models and influence their outputs. You should use inputs that are based on reliable sources, that are up to date, and that cover the relevant range and distribution of values. You should also verify your inputs by checking them for errors, outliers, and inconsistencies.

3 Reduce numerical errors

The third step to increase your simulation's accuracy is to reduce numerical errors that arise from the computational methods and algorithms you use to implement your models and inputs. Numerical errors are the differences between the exact and the approximate solutions of mathematical problems, and they can affect the accuracy and stability of your simulation. You should use numerical methods and algorithms that are suitable for your problem type, that have high accuracy and low error propagation, and that are optimized for your hardware and software. You should also monitor your numerical errors by using error estimates, convergence tests, and sensitivity analysis.

4 Perform validation and verification

The fourth step to increase your simulation's accuracy is to perform validation and verification of your simulation results. Validation is the process of checking whether your simulation results match the real world observations or expectations, and verification is the process of checking whether your simulation results match the intended model outputs. You should perform validation and verification throughout your simulation development and execution, using various methods and criteria, such as graphical analysis, statistical tests, and expert judgment. You should also document your validation and verification procedures and results.

5 Report uncertainty and confidence

The fifth step to increase your simulation's accuracy is to report uncertainty and confidence of your simulation results. Uncertainty is the measure of how much your simulation results may differ from the true values, and confidence is the measure of how much you trust your simulation results. You should report uncertainty and confidence of your simulation results using appropriate metrics and indicators, such as error bounds, confidence intervals, and probability distributions. You should also communicate uncertainty and confidence of your simulation results clearly and transparently to your stakeholders and users.

6 Update and improve your simulation

The sixth step to increase your simulation's accuracy is to update and improve your simulation based on new information, feedback, and learning. Simulation is an iterative and adaptive process, and you should always seek to improve your simulation by incorporating new data, models, inputs, methods, and algorithms, as well as by correcting errors, resolving issues, and addressing limitations. You should also evaluate your simulation performance and quality by using various measures and standards, such as accuracy, reliability, robustness, and efficiency.

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?