Exploring the behavior of complex industrial problems might become burdensome, especially in high-dimensional design spaces. Reduced Order Models (ROMs) aim to minimize the computational effort needed to study different design choices by exploiting already available data. In this work, we propose a methodology where the full-order solution is replaced with a Proper Orthogonal Decomposition based ROM, enhanced by a multi-fidelity surrogate model. Multi-fidelity approaches allow to exploit heterogeneous information sources, and consequently reduce the cost of creating the training data needed to build the ROM. To explore the multi-fidelity ROM capabilities, we present and discuss results and challenges for an automotive aerodynamic application, based on a geometric morphing of the DrivAer test case with multi-fidelity fluid-dynamics simulations.
