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

这个高风险、高回报的项目涉及基础设施网络决策支持的环境影响建模。该研究的智力价值在于引入了一个全新的建模框架，该框架通过网格计算和控制概念广泛捕获基础设施中的环境影响，并为该框架开发了一套分析和设计工具。建议的建模框架借鉴的影响模型，相互作用的马尔可夫链的结构化表示，可以捕捉复杂的时空模式，观察到的环境影响，但承认快速计算的本地统计数据，由于其矩线性特性。 通过建立影响建模结构，PI希望开发一个环境影响演变的混合模型。第二项关键任务是开发一套工具，以便在决策支持中使用环境影响模型，特别注重推理和分析工具。 首先，这项研究有可能影响许多广泛使用的基础设施中的计算和通信技术。 其次，我们的研究设想制定一个范式转变，在建模大规模计算，对模型，将控制理论的结构，允许分析和设计的网络（和多网络）动态。 计划开展教育和研究传播活动，以促进这项工作的社会和计算机科学影响。 重点包括多学科课程开发工作，以及为本科生提供的行业界面和研究经验。

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.