On Classification Issues within Ensemble-Based Complex System Simulation Tasks

Abstract

Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work we are developing a conceptual and technological approach to manage the ensemble-based simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions which can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition.