EAGER: SSDIM: Simulated and Synthetic Data for Interdependent Communications and Energy Critical Infrastructures

EAGER项目将开发新的数学基础和基于计算机的学习理论，用于生成广泛的模拟和完全合成的数据集，这些数据集对城市环境中相互依赖的通信和能源基础设施进行建模。这些增强的数据集和相关的数据构建工具将提供与相互依赖的关键基础设施（ICI）相关的大规模测试数据。新的模拟和合成数据生成工具将能够提高ICI的弹性和灵活性，提高其在极端天气条件和其他威胁下的安全性。该项目将涉及来自工程，计算机科学和心理学不同背景的学生，他们将接受与模拟和合成数据建模挑战相关的相关研究方法的培训。该教育计划包括一个新的以数据为中心的课程，称为创建模拟和合成数据的方法，以及研究生和本科生大规模参与大数据和智能社区研究。广泛传播是通过启用一个开放获取的数据库来确保的，这些数据库是根据资助研究的结果创建的，以及用于生成和分析这些数据的任何程序代码或相关工具。该开放获取测试平台既能满足主办机构的研究需求，也能满足其他用户对非专有多领域开放数据集的需求。该项目将为生成通信和能源等信息基础设施的模拟和合成数据奠定科学基础。我们的目标是开发模型，可以准确地重建，模拟和评估ICI功能的强大的理论框架，利用现有的现实世界的数据集。这项研究将带来几项创新：1）一种先进的迁移学习技术，使用可用的真实世界信息生成ICI的模拟数据，从而改善相互依赖性如何随着时间的推移形成或消失的特征; 2）基于分层贝叶斯方法的技术，用于创建合成数据，使ICI能够优化管理其共享资源，以应对日常故障，3）基于长期/短期记忆的深度学习方法，用于预测人类行为的人在回路认知建模的模拟数据，以及他们在应对涉及城市ICI的突发事件和事件时的决策效果;以及4）质量反馈回路验证和数据管理方法，通过将模拟和合成数据与具有大的规模模拟器，集成了城市环境中的ICI。

This EArly-concept Grant for Exploratory Research (EAGER) project will develop new mathematical foundations and computer-based learning theories for generating a wide range of simulated and fully-synthetic datasets that model interdependent communications and energy infrastructures in urban settings. These enhanced datasets and associated data building tools will provide a large-scale test data related to interdependent critical infrastructures (ICIs). New simulated and synthetic data generation tools will enable increasing the resiliency and flexibility of ICIs, improving their security during extreme weather conditions and other threats. This project will involve students from diverse backgrounds in engineering, computer science and psychology, who will be trained on pertinent research approaches related to the challenges of simulated and synthetic data modeling. The education plan includes a new data-centric course called Methods for Creating Simulated and Synthetic Data, as well as a large-scale involvement of graduate and undergraduate students in big data and smart community research. Broad dissemination is ensured by enabling an open-access repository of datasets created from the results of the funded research, as well as any program codes or related tools used to generate and analyze such data. The open-access testbed is capable of supporting both the research needs of the host institution as well as the requirement of non-proprietary multi-domain open datasets by other users.This project will develop a scientific basis for the generation of simulated and synthetic data on ICIs, such as communication and energy. The objective is to develop models that can accurately reconstruct, simulate, and evaluate a robust theoretical framework of ICI function by leveraging available real-world datasets. This research will lead to several innovations: 1) An advanced Transfer Learning technique that generates simulated data on ICIs using available real-world information, leading to improved characterization of how interdependencies can form or disappear over time; 2) a Hierarchical Bayesian method-based technique for the creation of synthetic data that enables ICIs to optimally manage their shared resources in response to failures from day-to-day operations, natural disasters, or malicious attacks; 3) a Long-/Short-term Memory-based deep learning method for predicting simulated data on human-in-the-loop cognitive modeling of human behavior and the effects of their decision-making in response to unexpected incidents and events involving urban ICIs; and 4) a quality-feedback loop verification and data management approach to fine-tune the simulated and synthetic data by comparing it against available real-world data over a realistic network with a large-scale simulator that integrates ICIs over an urban setting.