Video and Webinar Series

Reduced Order Modeling

Reduced order modeling (ROM) is a technique to simplify a high-fidelity mathematical model by reducing its computational complexity while preserving the dominant behavior of the complex model. This series highlights different applications of ROM and methods for creating reduced order models with MATLAB and Simulink.

4:30

ROM using Machine Learning Learn how to create reduced-order models of high-fidelity systems using machine learning techniques in System Identification Toolbox.

3:10

Reduced-Order Modeling Using Neural State Space Learn how to create a reduced-order model of an internal combustion engine using neural state-space identification approach from System Identification Toolbox.

Reduced Order Modeling: Examples and Software References