Department of Information Engineering, University of Padova, Padova, Italy.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Nat Commun. 2021 Mar 3;12(1):1429. doi: 10.1038/s41467-021-21554-0.
Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.
我们操纵复杂网络行为的能力取决于高效控制算法的设计，关键是取决于是否存在精确且易于处理的网络动力学模型。尽管近年来网络系统的控制算法设计取得了显著进展，但对网络动力学的了解是一个普遍存在的假设，在实践中很难满足。在本文中，我们克服了这一限制，开发了一种数据驱动的框架，以便在不了解网络动力学的情况下最优地控制复杂网络。我们的最优控制是使用有限数据集构建的，其中未知网络使用任意且可能是随机的输入进行刺激。尽管我们的控制对于具有线性动力学的网络是可证明正确的，但我们还针对噪声数据和存在非线性动力学的情况对其性能进行了特征描述，这些情况会出现在电网和脑网络中。
