Digital twins are very useful for answering or completing inquiries about the past or future behaviour of a complex cyber-physical system (of systems), which is either not yet fully implemented or is used remotely, whereby the creation of a physical local copy is not possible or economical. Digital twins are relatively inexpensive to create when they can be derived and simulated directly from artefacts created during development. However, these optimal conditions are often not (yet) given in practice. An automated creation would remedy this.
LearnTwins will establish a method kit for automated learning of trustable digital twins of cyber-physical systems (CPS). The method kit will provide new forms of model learning by combining automata learning, classical machine learning, deep learning, and explainable AI and will include automated test-case generation
Purpose of the learned digital twins is to reliably predict the behaviour and dependability of a CPS through simulation or characterization, to gain additional insights into the CPS, and to support its validation, verification and testing.
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