Directed network topologies of smart grain sensors

Abstract

We employ a recent technique for building complex networks from time series data to construct a directed network embodying time structure to collate the predictive properties of individual granular sensors in a series of biaxial compression tests. For each grain, we reconstruct a static predictive model. This combines a subset selection algorithm and an information theory fitting criterion that selects which other grains in the assembly are best placed to predict a given grain's local stress throughout loading history. The local stress of a grain at each time step is summarized by the magnitude of its particle load vector. A directed network is constructed by representing each grain as a nod..