부산대학교 도서관

Computer model validation is an essential step in simulation projects. The literature suggests using statistical techniques for comparing the outputs from the simulated model and the real system; however, statistical assumptions may be violated. Thus, Generative Adversarial Networks (GANs) are an alternative since they adapt to any data. The work aims to use GANs to generate synthetic data from the real data and use the Discriminator to discriminate real from simulated outputs. Five statistical distributions were trained, and distributions with the same characteristics were submitted to verify the Power of the Test. The curves of each distribution were generated. In addition, a real case of a Discrete Event Simulation in a large emergency department was applied to the new validation technique. The results showed that GANs effectively discriminate data and can help validate computer models.