Reduced order modeling is a computational methodology and research field focused on creating simplified representations of complex, high-dimensional systems. This involves generating models that approximate the essential behavior or input-output relationship of a system using a significantly reduced number of variables or degrees of freedom compared to a high-fidelity, full-order model. The field investigates techniques for effective dimensionality reduction, preservation of critical system characteristics, and handling parametric dependencies to enable computationally efficient simulation, analysis, optimization, and real-time control of intricate physical phenomena.