Prediction and classification in equation-free collective motion dynamics.

ABSTRACT: Modeling the complex collective behavior is a challenging issue in several material and life sciences. The collective motion has been usually modeled by simple interaction rules and explained by global statistics. However, it remains difficult to bridge the gap between the dynamic properties of the complex interaction and the emerging group-level functions. Here we introduce decomposition methods to directly extract and classify the latent global dynamics of nonlinear dynamical systems in an equation-free manner, even including complex interaction in few data dimensions. We first verified that the basic decomposition method can extract and discriminate the dynamics of a well-known rule-based fish-schooling (or bird-flocking) model. The method extracted different temporal frequency modes wi

SUBMITTER: Fujii K 

PROVIDER: S-EPMC6237418 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature