Applications and Methods | 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. Engineers use reduced order modeling to speed up system-level desktop simulations of large-scale first-principles models. ROMs are also useful for running real-time simulations for testing on hardware, modeling virtual sensors, and building digital twin applications. Explore different techniques for creating reduced order models with MATLAB^® and Simulink^® such as data-driven modeling, model-based ROMs, linearization-based methods, and physics-based reduction.