Beschreibung
Modeling Cyber-Physical Systems (CPS) is a challenging task as they are composed of heterogeneous components that interact with each other and with the physical environment. This inherent
complexity requires a high level of knowledge about the system and its environment when modeling CPS. We develop a framework for the automatic generation of models for CPS, which integrate
into tools for prediction, monitoring, and testing. This framework employs machine learning techniques to learn models from data or simulation. To learn models of arbitrary application areas
and different purposes, we use a modular approach that allows to support various learning techniques. This approach reduces the effort for designing, testing, and maintaining CPS in both
research and industry settings.
Keywords
modeling
prediction
monitoring
testing
CPS
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