This project aims to contribute to national health and prosperity by improving the modeling and analysis of large-scale simulation models.  Simulation is a widely used method to model stochastic systems. Still, analysis of these models becomes computationally and statistically difficult when the models involve a large number of potential input parameters.  To improve model tractability, it becomes essential to identify a subset of significant input parameters and then design effective simulation experiments using these input parameters.  This project will provide new input screening and sensitivity analysis techniques for improving the decision-making capability within performance and schedule requirements in large-scale, complex systems applications.  The techniques can apply to a wide range of application areas, such as biomedical studies, health care, manufacturing, and defense and homeland security operations.  This project will also positively impact engineering education and broaden the participation of underrepresented groups in the engineering enterprise.

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

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