Learning Digital Twins for the Validation and Verification of Dependable Cyber-Physical Systems.
The Austrian “ICT for the Future” project LearnTwins will establish a method kit for automated learning of trustable digital twins of cyber-physical systems (CPS). 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.
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 (for quality evaluation and learning data derivation), taking advantage of existing artefacts like log data, tests, and partial models.
A special focus of the learning approaches will be the explainability and understandability of the learned digital twins and a correct perception of their capabilities and limitations, supported by the involvement of psychologists and usability researchers. Practicability, generalizability and user acceptance of the method kit will be ensured by a user-centric research process and demonstrated and evaluated by applying it to three use cases.
Facts
Project duration: December 2020 – November 2023
Coordination: AIT
Budget: € 1,1 Million
Funding: € 770.000
Partner:
- Industry: AVL Graz
- Academia: TU Graz