Projects

Overview  

This project focuses on developing 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 metamodels, and (3) a metamodel-based global sensitivity analysis approach to quantifying the impact of each active input under heteroscedasticity. 

Here is the link to the project site: https://sites.google.com/view/chen-projects-smsa/home

Overview  

The project aims at developing a real-time, distributed decision-making algorithm, the Dynamic Network-based Probabilistic Route Planner, for the autonomous operation of multiple UAVs in congested environments. The key technical components include 1) a cloud-sourced database system for UAV data sharing, 2) an algorithm for virtual and dynamic UAV network discovery, 3) time-dependent nonparametric trajectory prediction models, and 4) a time-dependent collision probability map for conflict-free route generation.