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

EAGER：协作研究：基础设施网络自动决策的随机环境影响建模：多学科方法

• 批准号: 1058110
• 负责人: Yan Wan
• 金额: $ 10.06万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1058110&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Stochastic; Environmental

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.

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。这项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地捕捉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。所提出的建模框架借鉴了影响模型，这是一种相互作用的马尔可夫链的结构化表示，可以捕获在环境影响中观察到的复杂时空模式，但由于其矩线性特性，允许快速计算局部统计数据。通过建立影响模型结构，pi期望开发一个环境影响演化的混合模型。第二个关键任务是开发一套工具，允许在决策支持中使用环境影响模型，特别关注推理和分析工具。拟议研究的更广泛影响是双重的。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，我们的研究被设想为在大规模计算建模中制定一个范式转变，向包含控制理论结构的模型转变，以允许网络（和多网络）动力学的分析和设计。计划开展教育和研究传播活动，以促进这项工作的社会影响和与计算机科学有关的影响。重点包括多学科课程开发的努力，以及为本科生提供行业接口和研究经验。