Data-driven Control of Hypergraphs: Leveraging THIS to Damp Noise in Diffusive Hypergraphs
Robin Delabays, Yuanzhao Zhang, Florian Dörfler, and Giulia De Pasquale
可控性决定了系统的状态是否可以引导到任何所需的配置,使其成为设计有效控制策略的基本先决条件。 在联网系统的背景下,可控性是一个公认的概念。 然而,许多现实世界的系统,从生物集体到工程基础设施,都表现出高阶相互作用,无法通过简单的图形捕获。 此外,代理人相互作用和相互影响的方式往往是未知的,必须从对系统的部分观察中推断出来。 在这里,我们关闭了超图表示与我们最近开发的超图推理算法之间的循环,这,以推断底层多体联轴。 基于推断的结构,我们设计了一个吝啬的控制器,给定一组最小的可控节点,引导系统走向所需的配置。 我们在通过超图进化的Kuramoto振荡器网络上验证了拟议的系统识别和控制框架。
Controllability determines whether a system's state can be guided toward any desired configuration, making it a fundamental prerequisite for designing effective control strategies. In the context of networked systems, controllability is a well-established concept. However, many real-world systems, from biological collectives to engineered infrastructures, exhibit higher-order interactions that cannot be captured by simple graphs. Moreover, the way in which agents interact and influence one anoth...
