CAREER: Model-Free Input Screening and Sensitivity Analysis in Simulation Metamodeling

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This Faculty Early Career Development Program (CAREER) award will contribute to national health and prosperity by improving modeling and analysis using large-scale simulations. Simulation is a widely used method to model stochastic systems, but analysis of these models becomes computationally and statistically difficult when the models involve a large number of potential input parameters. In order to improve model tractability, it becomes important to identify a subset of significant input parameters, and then to 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 have the potential to 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. The investigator will develop methods to enhance the scalability and sampling efficiency of metamodel-based simulation analysis. The key technical components include (1) a model-free method based on the Morris elementary effects method for sequential input screening with rigorous statistical performance guarantees, (2) theory and methods for constructing knowledge- and data-driven scalable heteroscedastic dual metamodels, and (3) a rigorous metamodel-based global sensitivity analysis approach to quantifying the impact of each active input under heteroscedasticity with finite-sample and large-sample performance guarantees. This project will generate novel methodology and practical algorithms for effective and efficient online input screening and global sensitivity analysis. The methods and algorithms developed will be tested on complex stochastic systems through two ongoing research collaborations with biomedical informatics researchers. The educational plan includes development of pre-college outreach programs, undergraduate and graduate curricula, and career development-related workshops for female engineering students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.