What is reduced-order models?

Reduced-order models are a mathematical tool used in engineering and scientific fields to simplify complex systems and processes. They are lower-dimensional models constructed from high-dimensional systems by reducing the number of variables and identifying the most important parameters that govern the system's dynamics.

These models are obtained through various mathematical techniques, such as model reduction techniques, data-driven methods, and statistical analysis. The reduced-order models are used to analyze and study the system's behavior and predict its response under different conditions.

Reduced-order models are particularly useful in applications where the full model is computationally expensive or cumbersome, such as in aerodynamics, structural analysis, fluid dynamics, and control system design. They have a wide range of applications across various industries, including automotive, aerospace, biomedical, and electronics.

The accuracy of reduced-order models depends on the quality of the data used, the number of variables used, and the model reduction method employed. Therefore, it is essential to carefully select the model reduction technique and validate the reduced-order model's results against the original high-dimensional model.