During vehicle development, high-fidelity models such as those based on finite element analysis, computer-aided engineering, and computational fluid dynamics are created for a variety of components. However, these high-fidelity models are not suitable for all stages of the development process. For example, a finite element analysis model that is useful for detailed component design will be too slow to include in system-level simulations for verifying your control system or to perform system analyses that require many simulation runs. Similarly, a high-fidelity model for the thermal behavior of a battery will be too slow to run in real time on your embedded system.
Does this mean you have to start from scratch to create faster approximations of your high-fidelity models? This is where reduced-order modeling (ROM) comes to the rescue. ROM is a set of computational techniques that helps you reuse your high-fidelity models to create faster-running, lower-fidelity approximations.
This talk focuses on AI-based ROM techniques and methods and how they can be leveraged for Model-Based Design. Discover how to leverage the Simulink® add-on for reduced-order modeling to set up design of experiments, generate input-output data, and train and evaluate suitable reduced-order models using preconfigured templates that cover various ROM techniques. Learn how to integrate these AI models into your Simulink simulations, whether for hardware-in-the-loop testing or deployment to embedded systems for virtual sensor applications. Explore the pros and cons of different ROM approaches to help you choose the best one for your next project.
