Gfeller David, De Los Rios Paolo
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, Canada M5S 3E1.
Phys Rev Lett. 2008 May 2;100(17):174104. doi: 10.1103/PhysRevLett.100.174104.
Coarse graining techniques offer a promising alternative to large-scale simulations of complex dynamical systems, as long as the coarse-grained system is truly representative of the initial one. Here, we investigate how the dynamical properties of oscillator networks are affected when some nodes are merged together to form a coarse-grained network. Moreover, we show that there exists a way of grouping nodes preserving as much as possible some crucial aspects of the network dynamics. This coarse graining approach provides a useful method to simplify complex oscillator networks, and more generally, networks whose dynamics involves a Laplacian matrix.
只要粗粒化系统能真正代表初始系统，粗粒化技术就为复杂动力系统的大规模模拟提供了一种很有前景的替代方法。在此，我们研究当一些节点合并在一起形成粗粒化网络时，振子网络的动力学特性是如何受到影响的。此外，我们表明存在一种对节点进行分组的方法，能尽可能多地保留网络动力学的一些关键方面。这种粗粒化方法为简化复杂振子网络，更一般地说，为简化其动力学涉及拉普拉斯矩阵的网络，提供了一种有用的方法。
