Sensitivity Analysis and Monte Carlo Simulations using Simulink Design Optimization

When you are working with large and complex Simulink models, it is sometimes difficult to determine which model parameters impact behavior   the most. Using Monte Carlo simulations, correlation techniques and design of experiments (DoE), Sensitivity Analysis allows you to determine which parameters have the greatest impact on your model.

In this webinar, we will use an example to demonstrate how to analyze and visualize your model's behavior across its design space using Monte Carlo simulations. This will help you identify which parameters impact characteristics such as step response times, energy consumption and component failure rates.

You can also use sensitivity analysis to improve design optimization performance. Using an example, we will see how you can identify a good initial point and a smaller set of parameters in a large model, allowing you to reduce the time taken for the optimization process.