We present and analyze a methodology for numerical homogenization of spatial networks, modelling e.g. diffusion processes and deformation of mechanical structures. The aim is to construct an accurate coarse model of the network. By solving decoupled problems on local subgraphs we construct a low dimensional subspace of the solution space with good approximation properties. The coarse model of the network is expressed by a Galerkin formulation and can be used to perform simulations with different source and boundary data at a low computational cost. We prove optimal convergence of the proposed method under mild assumptions on the homogeneity, connectivity, and locality of the network on the coarse scale. The theoretical findings are numerically confirmed for both scalar-valued (heat conduction) and vector-valued (structural) models.
翻译:我们提出并分析空间网络数字同质化的方法,例如扩散过程和机械结构变形等建模,目的是构建一个准确的网状模型。通过解决本地子层分解的问题,我们建造了一个具有良好近似特性的解决方案空间的低维次空间。网状粗度模型以Galerkin的配方表示,可用于以低计算成本对不同源数据和边界数据进行模拟。我们证明,在对网络的同质性、连通性和广度的微小假设下,拟议方法最优化地趋同。理论结论在数字上证实了标价(热导)和矢量值(结构)模型。