Harnessing Network Dynamics for "Smart" Infrastructure and Urban Resilience

动机：大多数大型复杂系统都具有对级联故障的固有脆弱性。在这些过程中，一个最初很小的扰动被允许传播和放大，最终导致系统的大部分改变行为或失败。例子包括停电、物种灭绝和金融恐慌。因此，级联预测和防御在一系列应用中至关重要。 研究问题：几十年的研究提供了大量关于级联故障的程式化见解，包括生成机制和统计特性。然而，许多实际应用需要预测单个事件的能力，这反过来又需要一些系统初始状态和详细动态的知识。后者代表了一个特殊的障碍，研究级联的“系统的系统”（如电力+运输系统），在详细的建模理解不同系统之间的耦合仍然难以捉摸。 研究目的：该研究计划的目标是更深入地了解级联故障的动力学基础，特别是在多层系统中。我们将通过三个相互关联的短期研究目标来实现这一目标，使用基础设施网络作为激励应用：1）了解电力/交通系统中的个体电力/交通故障如何引起人类行为的变化; 2）量化可观测性的实际限制，（或对手的）能力，以确定;和最后3）设计自适应，自愈电力系统的基础上随时间变化的网络。 预期结果：在拟议的5年研究计划结束时，我们将确定可用于预测、监测和应对基础设施系统内部和之间大规模故障的特定动态特征。我们的长期目标是建立在这些见解的基础上，朝着城市基础设施的准确“系统的系统”建模发展，该模型考虑了不同基础设施层之间的结构和动态异质性以及人类行为的影响。 影响：这里产生的见解将推进复杂系统中动态推理、故障建模和控制的最新发展。在此过程中，它将为工程师和政策制定者提供新的工具，以加强城市基础设施，抵御年龄、气候变化和蓄意攻击带来的威胁。此外，预计这些工具将提供对相关网络问题的见解，包括可持续生态系统和金融系统管理。

Motivation: Most large complex systems possess an inherent vulnerability to cascading failures. In these processes, an initially small disturbance is allowed to propagate and amplify, eventually causing large parts of the system to change behavior or fail. Examples include power outages, waves of species extinctions, and financial panics. As such, cascade forecasting and defense is of paramount importance in a range of applications. Research Problem: Decades of research have provided a trove of stylized insights about cascading failures, including generative mechanisms and statistical properties. Yet many practical applications demand the ability to forecast individual events, which in turn demands some knowledge the systems initial state and detailed dynamics. The latter represents a special obstacle to the study of cascades in “systems of systems” (such as the power + transportation systems), where detailed modeling understanding of the couplings between different systems remains elusive. Research Objectives: The goal of this research program is to generate deeper understanding of the dynamical foundations of cascading failures, specifically in multilayer systems. We will pursue this through three interrelated short-term research objectives, using infrastructure networks as a motivating application: 1) understanding how individual power/transportation failures in power/transportation systems induce changes in human behavior; 2) quantifying the practical limits of observabilityour (or an adversary's) ability to determine ; and finally 3) designing adaptive, self-healing power systems based on time-varying networks. Anticipated Outcomes: By the end of the proposed 5-year research program, we will have identified specific dynamical features that can be exploited to predict, monitor, and counteract large-scale failures both within and between infrastructure systems. Our long-term goals are to build on these insights, moving toward accurate “systems-of-systems” modeling of urban infrastructure that accounts for the structural and dynamical heterogeneities between different infrastructure layers as well as the influence of human behavior. Impact: The insights generated here will advance the state of the art of dynamical inference, failure modeling, and control in complex systems. In the process, it will provide engineers and policymakers with new tools to fortify urban infrastructure against the threats posed by age, climate change, and deliberate attack. In addition, these tools are expected to provide insights into related network problems, including sustainable ecosystem and financial system management.