Reduced Order Modeling: Applications and Methods
From the 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. 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.
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