Dynamic data driven simulation is a simulation paradigm which integrates simulation and data together.This paradigm continuously feeds real-time data into the simulation,enabling the simulation be dynamically adjusted by the data,which thus improves the simulation-based estimation and prediction capability.Due to this integration,the dynamic data driven simulation can estimate system states and predict future state evolution more accurately.This paper reviews the origins and basic concept of dynamic data driven simulation,and introduces several simulation paradigms originated from the idea of"integrating models with data",and identifies the linkages and differences among them.The particle filter-based data assimilation method and the identical-twin simulation experiment are introduced.The current research status of dynamic data driven simulation is summarized from four perspectives,i.e.,application scenarios,models and data,data assimilation algorithms,and integration with new technologies.Finally,the future research directions are outlooked from five aspects,which are simulation models,measurement data,data assimilation,algorithm performance,and application areas.
modeling and simulationdynamic data driven simulationdata assimilationparticle filtersidentical-twin simulation experiment
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