This project aims to contribute to national health and prosperity by improving the modeling and analysis of large-scale simulation models. Simulation is widely used to represent stochastic systems; however, analyzing such models becomes computationally and statistically challenging when they involve a large number of potential input parameters. To improve tractability, it is essential to identify a subset of influential inputs and design effective simulation experiments focused on these parameters. This project will develop new simulation metamodeling and sensitivity analysis techniques to enhance decision-making within performance and schedule constraints in large-scale, complex-system applications. The resulting methods are broadly applicable across domains such as biomedical research, healthcare, and manufacturing. The project will also strengthen engineering education and broaden participation of underrepresented groups in the engineering enterprise.

This project is supported by the National Science Foundation CAREER under Grant No. CMMI-1846663.