AI with Model-Based-Design: Reduced Order Modeling

High-fidelity models, such as those based on FEA (Finite Element Analysis), CFD (Computational Fluid Dynamics), and CAE (Computer-Aided Engineering) models can take hours or even days to simulate and are not suitable for all stages of development. 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.

A high-fidelity model for analyzing NOx emissions will be too slow to run in real time in your embedded system. 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.

In this session, you will learn how to create AI-based reduced order models to replace the complex high-fidelity model of a jet engine blade. Using the Simulink add-on for Reduced Order Modeling, see how you can perform a thorough design of experiments and use the resulting input-output data to train AI models using pre-configured templates of LSTMs, neural ODE, and nonlinear ARX. Learn how to integrate these AI models into your Simulink simulations for control design, Hardware-in-the-Loop (HIL) testing, or deployment to embedded systems for virtual sensor applications.