Distributed Dynamic Data Driven Simulations: Basic Idea and an Illustration Example

The dynamic data driven simulation (DDDS) is a simulation paradigm where the simulation is continuously influenced by fresh data sampled from the real system for better analysis and prediction of the system under study. Traditional DDDS operates in the “computation away from data” mode, which would incur long response time, high network bandwidth requirement, and critical information loss. This paper proposes a novel distributed dynamic data driven simulation paradigm. In this paradigm, each participated simulation models a portion of a system, while all the participants collectively model the whole system. Additionally, each simulation assimilates data collected locally to produce local state estimations, which are aggregated somehow to generate global state estimation. The proposed simulation paradigm is supposed to have properties such as shorter response time, lower network bandwidth requirement, and less information loss. Finally, a case is studied to illustrate the effectiveness of the novel simulation paradigm.