부산대학교 도서관

The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation. Another approach is understanding the target system by using a simulation modeled by human knowledge with interpretable simulation parameters. Recently, simulator calibration has been developed based on kernel mean embedding to estimate the simulation parameters as posterior distributions. Our idea is to use a simulation model as an interpretable surrogate model. However, the computational cost of simulator calibration is high owing to the complexity of the simulation model. Thus, we propose a ''model-bridging'' framework to bridge machine learning models with simulation models by a series of kernel mean embeddings to address these difficulties. The proposed framework enables us to obtain predictions and interpretable simulation parameters simultaneously without the computationally expensive calculations of the simulations. In this study, we apply the proposed framework to essential simulations in the manufacturing industry, such as production simulation and fluid dynamics simulation.