Abstract
Holistic simulation aids the engineering of cyber physical systems. However, its complexity makes it expensive regarding computation time and modeling effort. We introduce multi-level-simulation (Our Multi-Level-Simulation approach was already published in [1]. The description of our approach in this paper is based on this publication and updates it. This description is the context to the results on learning State mappings within Multi-Level-Simulations presented in this paper.) as a methodology to handle this complexity. In this methodology, the required holistic perspective is reached on a coarse level, which is linked with multiple detailed models of small sections of the system. In order to co-simulate the levels, mappings between their states are required. This paper gives an insight into the current state of progress of using well known machine learning techniques for regression to generate these mappings using small sets of labeled training data.
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Acknowledgments
We thank the Simulationswissenschaftliches Zentrum Clausthal-Göttingen (SWZ) for financial support.
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Wittek, S., Rausch, A. (2018). Learning State Mappings in Multi-Level-Simulation. In: Baum, M., Brenner, G., Grabowski, J., Hanschke, T., Hartmann, S., Schöbel, A. (eds) Simulation Science. SimScience 2017. Communications in Computer and Information Science, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-319-96271-9_13
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DOI: https://doi.org/10.1007/978-3-319-96271-9_13
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