EAGER: Collaborative Research: Stochastic Environmental-Impact Modeling for Automated Decision-Making in Infrastructure Networks: A Multi-Disciplinary Approach

Washington State University, Pullman WA

Investigators

Abstract

This high-risk, high-reward project is concerned with environmental-impact modeling for infrastructure-network decision-support. The intellectual merit of the research lies in the introduction of an entirely new modeling framework which broadly captures environmental impact in infrastructures by meshing computing and controls concepts, and in the development of a suite of analysis and design tools for this framework. The proposed modeling framework draws on the influence model, a structured representation of interacting Markov chains that can capture the intricate spatio-temporal patterns that are observed in environmental impact, and yet admits rapid computation of local statistics due to its moment-linearity characteristic. By building on the influence-modeling construct, the PIs expect to develop a hybrid model for environmental-impact evolution. A second key task is to develop a suite of tools that allow the use of the environmental-impact model in decision support, with a particular focus on inference and analysis tools. The broader impact of the proposed research is twofold. First, the research has the potential to impact computation and communication technologies in numerous widely-used infrastructures. Second, we the research is envisioned to enact a paradigm shift in modeling for large-scale computation, toward models that incorporate control-theoretic constructs to permit analysis and design of network (and multi-network) dynamics. Education and research-dissemination activities are planned to foster both the societal and computer-science-related impacts of the work. Highlights include multi-disciplinary course-development efforts, and industry-interface and research-experience provisions for undergraduates.