Research Projects

The overall idea of AIDOaRT is to efficiently support requirements, monitoring, modelling, coding, and testing activities during the software development process. AIDOaRT can be used as a platform to extend existing tools. To this intent, the project proposes the use of Model-Driven Engineering (MDE) principles and techniques to provide a model-based framework offering proper methods and related tooling. The projects’ framework will enable the observation and analysis of collected data from both runtime and design time to provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases involving complex CPSs.
Beginn: 31.03.2021
Ende: 30.03.2024
Objectives and Motivation: • The estimation of energy consumption of a driver moving along some road currently is based on statistical methods that give good but not very precise forecasts. Within this Project, these forecasts shall be made much more precise without losing explainability or reliability. Methodology: • Based on real-driver data and combinations of automata-learning and machine learning methods, more accurate models of driver behaviour shall be constructed that give more precise forecasts. For comparison purposes a purely deep-learning approach also is followed. Expected Results: • More precise driver models that are able to predict the energy usage over arbitrary roads and given a certain driving “style” more precisely, while still retaining modest computational overhead and being “real-time” (i.e., no GPUs required, couple of minutes for the computation).
Beginn: 31.01.2023
Ende: 30.01.2024
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 physicallocal 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, e.g. by learning methods, would remedy this. In addition, for successful use, the applied models and the insights derived from them must sufficiently reflect the properties of the real system. If this is not the case, they are even counterproductive as they lead to wrong conclusions. Therefore, one must be able to trust digital twins. Hence, they must be correct and reliable and at the same time cost-effective in their creation and maintenance. The use of a new technology is often accompanied by doubts about its reliability and concerns about possible side effects. If digital twins are created automatically, e.g. by learning methods, the process behind it is not easy to understand for the user. For acceptance of the technology by the affected user group, instruments must be available to correctly assess the reliability, traceability and limitations of digital twins and to establish trust. LearnTwins addresses these mentioned challenges by using a combined learn-based testing method. This is based on the insight that the properties of complex systems often cannot be captured concisely in a single model (type). Therefore, the project aims to combine different learning methods to create the digital twin (automatic learning, classical machine learning and deep learning). In addition to already existing data sources, learning data will be gained by executing test cases on the real system, whereby the test cases, in turn, will be created automatically from the (learned) digital twin. The technical work will be embedded in a foresight process. For this purpose, the involvement of stakeholders is planned, who actively work out desired futures and strategies regarding the developed technology. The results of the project will enable the faster and more economical creation of high-quality and reliable digital twins and accelerate the necessary digital transformation of product artefacts. The results on the understandability of automatically learned models should contribute to a higher acceptance and a more focussed use of learning-based methods. The developed methods will be tested and evaluated in three realistic use cases from different domains.
Beginn: 30.11.2020
Ende: 29.11.2023