Reduced-order modeling is a powerful technique in which small yet accurate models of complicated systems are derived. The basic idea is to start with a high-order model (e.g. a CFD model of a fluid flow), extract a reduced-space basis which captures the desired dynamics accurately, and project the high-order system onto the reduced space to obtain a low-order model. By considering the inputs and outputs that are relevant for the particular application, a huge reduction in the number of states can be obtained, e.g. for a two-dimensional Euler aeroelastic model a reduction from 300,000 to 200 flow states was achieved while retaining the required accuracy. The basis can be calculated via a variety of methods, including eigenmodes, proper orthogonal decomposition, Arnoldi methods and balanced truncation.