Uncertainty Propagation Methods for Networked Complex Systems
Rahul Rai Principal Investigator
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The objective of this research is development of a novel class of uncertainty quantification methods for networked complex systems. The fundamental and challenging uncertainty quantification problems remain unsolved, in particular combating the curse of dimensionality and solving uncertainty quantification problems related to large and multi-scale dynamic networks underlying modern day complex systems. The main challenge that lies at the core of analyzing and synthesizing the dynamic networks at the crux of modern day complex systems is: How do a collection of dynamical systems coupled through a dense wiring topology behave as a unit in the presence of uncertainty? The UB-SUNY team is developing of a suite of novel computational uncertainty quantification methods to tackle the main challenge and to enable accelerated design and deployment of complex systems.<br/><br/>This project will have far reaching impacts on research and the practice of complex system design and management across a wide variety of industries. Development of these methods will have an immediate impact on the community at large with beneficiaries ranging from ordinary users to system design teams in industrial, and academic environments. Our research is being integrated into the education of graduate, undergraduate and high-school students including under-represented minorities in science and engineering fields. These educational activities involve: 1) developing workshops and short course for researchers and professionals working in the area of systems engineering and uncertainty quantification, 2) developing seminar courses for undergraduates, and 3) incorporation of research results in graduate/undergraduate courses.