# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
Footnote †: journal: Applied Sciences  
[PERSON]  
1 Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; [EMAIL_ADDRESS] (S.H.C. ); ;  
faithfulness with which model behavior reflects modeled system behavior [10; 11]. For the models that represent the same reference system, fidelity is compared by their outputs for the same input. In other words, a high-fidelity model has a high accuracy in model output compared to the reference system, whereas a low-fidelity model has a relatively less accurate output. Because the high-fidelity simulation is very time-consuming due to the high computational load, an appropriate portion of a simulation scenario requires simulating with high-fidelity models. To decrease the simulation time without significant loss in respect to simulation accuracy, we propose a multi-fidelity modeling framework. The proposed framework consists of a dynamical conversion process between high- and lower-fidelity models. It employs a fidelity change approach to select a suitable model from a pool of models with variable fidelities. Within a single simulation scenario (i.e., a simulation design point), all the time frames need not be simulated with high-fidelity models [12]. Therefore, the high-fidelity models are primarily used for important regions of the scenario, whereas the lower-fidelity models are used for marginal parts. In this context, the point of our framework is to decide which model needs to change its level of fidelity and to know when to change the fidelity within the simulation scenario. To this end, we define three concepts in the framework: (1) an interest region where the model output has a serious effect on the overall simulation analysis, (2) a fidelity change condition (FCC) to check whether the fidelity of the model needs to be changed based on the defined interest region, and (3) a selection model to choose a model with the proper fidelity when the FCC is satisfied. The proposed framework, based on these concepts, allows the achievement of a condition-based transformation between high- and low-fidelity simulations within a scenario, reducing the overall computational cost and ensuring the accuracy of the simulation analysis. The targeted system of this paper is a discrete event dynamic system. It exhibits hybrid behaviors characterized by a set of continuous laws that are switched by discrete events [13]. The dynamic property facilitates the development of apparent high- and low-fidelity models for a target system, and the discrete event property allows one to know when the level of fidelity is changed with discrete events. In our framework, these concepts are proposed with formal modeling specifications. In particular, this paper uses the discrete time system specification (DTSS) for the dynamic system model and the discrete event system specification (DEVS) for the discrete event system [14]. Such a formal modeling approach facilitates maximizing the reusability of existing models and simulation algorithms. In recent years, multi-fidelity modeling and simulation (M&S) have been studied in many application fields such as aerospace engineering [15], submarine systems [16], computational fluid dynamics [17], and supply chain systems [18], etc. A common point of these studies is to drive the preliminary design process using low-fidelity models as surrogates for expensive high-fidelity simulations. High-fidelity models are then used in the final design stages to refine the design. This means that the fidelity change has not occurred during the same simulation scenario. In addition, these studies focused on application-oriented methods, which have key differences from our study. For experimentation, we performed two practical applications that show the effectiveness of the proposed framework in simulation speedup and accuracy.