# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 5 Applications
### UTV Simulation
#### 5.2.2 Multi-Fidelity Modeling Process  
The first step is selecting a target model for multi-fidelity modeling. Based on the two prerequisites for the selection, we chose the UTV model as the target model, because it is modeled by the DEVS and has the highest _ETR_: 0.98 (i.e., it requires high computational complexity to find the shortest path using the Dijkstra algorithm, and it is executed continuously over the entire simulation time). To choose the interest region of the UTV model, we used the state variable of \"_Distance_\" as the IRV. It is a positive number that represents the expected distance between a passenger's departure vertex and arrival vertex. In the UTV simulation, the shortest path of a vehicle has a great effect on the accuracy of the simulation results. As the expected distance increases, the shortest path found by the Dijkstra algorithm becomes more complex; thus, the interest region of the UTV model was defined when \"_Distance_\" had a high value. However, the exact criteria for this high value can vary depending on the low-fidelity model. In this case study, we decided this value experimentally with a consideration of the trade-off relation between the execution time of the UTV simulation and the accuracy of the simulation results (see the fourth step of the applied framework).  
The third step is to develop a lower-fidelity model of the target model. We developed the low-fidelity UTV model by simplifying the time advance function, as shown in Figure 8. The existing UTV model (i.e., the high-fidelity model) calculates the state time of \"_BUSY_M_\" and \"_WAIT_M_\" using the shortest path found by the Dijkstra algorithm, whereas the low-fidelity model simply uses the Euclidean distance between the starting and ending vertex. The fourth step is to composite a multi-fidelity model based on the specification in Section 4.3.2, as shown in Figure 17. The high-fidelity model \(M_{1}\) is the existing UTV model, and the low-fidelity model \(M_{2}\) is the developed lower-fidelity model in the third step. As mentioned previously, we have to decide the exact value that separates the interest region to determine the _FCC_.  
To this end, for the composited multi-fidelity model, we measured the simulation execution time and the accuracy of the simulation results according to the various fidelity change values of \"_Distance_\", as shown in Figure 18. The accuracy here is the relative accuracy to the existing UTV simulation model (i.e., the high-fidelity model). As the change value increases, the number of executions of the low-fidelity model increases in the multi-fidelity UTV model; thus, the execution time decreases, but the accuracy is also lowered. Assuming that the permissible accuracy loss is under 0.05, the possible candidates for the  
Figure 17: Overall process of the multi-fidelity modeling framework: UTV simulation as an example of the discrete event system.  
change value are 1000, 1500, and 2000. To maximize the speedup of the multi-fidelity model, the change value was decided as 2000 with the minimum execution time. As a result, the _FCC_ was composed with \"_Distance_\" and its value: 2000. The last step is to substitute the existing UTV model with the composed multi-fidelity model without any modifications to the other models in the UTV simulation model.