Learning, Modeling, and Applying Cooperation Mechanisms of Complex Systems

The Ensemble Kalman filter (EnKF) is a classic method of data assimilation. For distributed sampling, the conventional EnKF usually requires a centralized server to integrate the predictions of all particles or a fully-connected communication network, causing traffic jams and low bandwidth utilization in high-performance computing. In this paper, we propose a novel distributed scheme of EnKF based on network setting of sampling, called Particle Network EnKF. Without a central server, every sampling particle communicates with its neighbors over a sparsely connected network. Unlike the existing work, this method focuses on the distribution of sampling particles instead of sensors and has been proved effective and robust on numerous tasks. The numerical experiments on the Lorenz-63 and Lorenz-96 systems indicate that, with proper communication rounds, even on a sparse particle network, this method achieves a comparable performance to the standard EnKF. A detailed analysis of effects of the network topology and communication rounds is performed. Another experiment demonstrating a trade-off between the particle homogeneity and performance is also provided. The experiments on the whole-brain neuronal network model show promises for applications in large-scale assimilation problems.