While the growing number of computational models available to designers can solve a lot of problems, it complicates the process of properly utilizing the information provided by each simulator. It may seem intuitive to select the model with the highest accuracy, or fidelity. Decision makers want the greatest degree of certainty to increase their efficacy. However, high fidelity models often come at a high computational expense. While comparatively lacking in veracity, low fidelity models do contain some degree of useful information that can be obtained at a low cost. We propose a method to utilize this information to generate a fused model with superior predictive capability than any of its constituent models. Our methodology estimates the correlation between each model using a model reification approach that precludes the need for observational data. The correlation is then used in an updating procedure whereby uncertain outputs from multiple models may be fused together to better estimate some quantity or quantities of interest.
