The HPC community has been using abstract, representative applications and architecture models to enable faster co-design cycles. While developers often qualitatively verify the correlation of the application abstractions to the parent application, it is equally important to quantify this correlation to understand how the co-design results translate to the parent application. In this paper, we propose a multi-fidelity surrogate (MFS) approach which combines data samples of low-fidelity (LF) models (representative apps and architecture simulation) with a few samples of a high-fidelity (HF) model (parent app). The application of MFS is demonstrated using a multi-physics simulation application and its proxy-app, skeleton-app, and simulation models. Our results show that RMSE between predictions of MFS and the baseline HF models was 4%, which is significantly better than using either LF or HF data alone, demonstrating that MFS is a promising approach for predicting the parent application performance while staying within a computational budget.