Uncertainty Propagation Methods for Networked Complex Systems

本研究的目的是为网络化复杂系统开发一类新的不确定性量化方法。基本的和具有挑战性的不确定性量化问题仍然没有得到解决，特别是与维数诅咒和解决与现代复杂系统基础的大型和多尺度动态网络相关的不确定性量化问题。分析和综合现代复杂系统的核心动态网络的主要挑战是：在存在不确定性的情况下，通过密集布线拓扑耦合的动态系统的集合如何作为一个单元表现？UB-SUNY团队正在开发一套新颖的计算不确定性量化方法，以解决主要挑战，并加速复杂系统的设计和部署。这个项目将对研究和实践的复杂系统的设计和管理在各种各样的行业产生深远的影响。这些方法的发展将对整个社区产生直接影响，受益者从普通用户到工业和学术环境中的系统设计团队。我们的研究正被整合到研究生、本科生和高中生的教育中，包括在科学和工程领域代表性不足的少数族裔。这些教育活动包括：1)为在系统工程和不确定性量化领域工作的研究人员和专业人员开发讲习班和短期课程，2)为本科生开发研讨会课程，以及3)将研究成果纳入研究生/本科生课程。

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.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.