Scientific publications

This document describes fundamental considerations towards a process for identification of relevant parameters for a digital twin. The process includes identifying parameters in the real-world product (physical twin) which are relevant for its digital twin given a specific use case and describing methods to acquire and use these parameters.

The optimization of complex cyber-physical systems is a crucial task for their correct functioning, usability, and commercial viability. Due to their complexity, scale and resource intensiveness, conventionalmanual optimization is infeasible in many instances. We investigate the combination of the Digital Twin paradigm and Reinforcement Learning framework to address the long response times, limited availability ofdata, and the intractability of such systems.

High-tech cyber-physical systems (CPS) are becoming increasingly diverse: they may have many variations and configurations, might be part of product families, and may be highly customizable. Additionally, CPSs tend to continuously evolve—have variation in time—for example due to technology updates, changing demands, or changing requirements.

The performance of cyber-physical systems (CPS) is a determining factor for their success and often needs to be guaranteed. When performance issues occur, their analysis and the identification of the root-cause should be fast. Typically the analysis requires the relation of system observations (i.e., tracing) to design and implementation artifacts.

Compositionality supports the manipulation of large systems by working on their components. For model-based testing, this means that large systems can be tested by modelling and testing their components: passing tests for all components implies passing tests for the whole system. In previous work [14], we defined mutual acceptance for specification models and proved that this is a sufficient condition for compositionality in model-based testing.

We combine Behavior-Driven Development (BDD) and formal Model-Based Testing (MBT), to benefit from the smooth collaboration among stakeholders in BDD, and from automated testing with precise test cases in MBT. However, textual BDD scenarios written in natural language are not sufficient for formal MBT, as they might be ambiguous and lack the required information for testing.

Model-based testing (MBT) promises a scalable solution to testing large systems, if a model is available. Creating these models for large systems, however, has proven to be difficult. Composing larger modelsfrom smaller ones could solve this, but our current MBT conformance relation uioco is not compositional, i.

The functionality that we use as organizations and citizens increasingly arises from a complex interplay of man-built systems, individuals and organizations, and the environment. It is a challenge to get the desired functionality and features consistently, reliably and affordably, without unwanted side effects.

In the search for appropriate solutions, architects and stakeholders need ways to reason about concepts and their impact. The understanding, communication, and reasoning facilitates decision making. In this keynote, we explore conceptual models as the means to achieve all of these needs. We will make the abstract notion of conceptual models concrete by using future energy systems as an application area.