<p>Our society is surrounded by technical systems that increasingly influence and<br />take over aspects of everyone’s everyday life. As we build on the correct functioning, the need for appropriate methodologies that support the development of reliable products persists. However, the development and in particular the qualification become challenging due to their increasing inherent complexity. Virtual testbeds and digital twins are modern key concepts in this context and offer a wide range of opportunities for simulation-based verification and validation processes with virtual prototypes, especially in cases where real tests are hard to perform. However, the validity of test results derived from virtual experiments depends on the quality of the underlying simulation models.<br />Of course, the models themselves must be verified before. But in the context<br />of virtual testbeds and digital twins this becomes challenging since the corresponding models are usually complex themselves. Therefore, we presented an approach to establish structured verification and validation activities for digital twins and virtual testbeds, based on a modular configuration framework that supports the development of complex simulation models.</p>
The growing relevance of artificial intelligence (AI) for technical systems offers significant potential for the realization and operation of autonomous systems in complex and potentially unknown environments. However, unlike classical solution approaches, the functionality of an AI system cannot be verified analytically, which is why data-driven approaches such as scenario-based testing are used. With the increasing complexity of the required functionality of the AI-based system, the quantity, and quality of the data needed for development and validation also increase. To meet this demand, data generated synthetically using simulation is increasingly being used. Compared to the acquisition of real-world reference data, simulation offers the major advantage that it can be configured to test specific scenarios of interest. This paper presents an architecture for the systematic generation of virtual test scenarios to establish synthetically generated test data as an integral part of the development and validation process for AI systems. Key aspects of this architecture are the consistent use of digital twins as virtual 1-to-1 replicas and a simulation infrastructure that enables the generation of training and validation data for AI-based systems in appropriate quantity, quality, and time. In particular, this paper focuses on the application of the architecture in the context of two use cases from different application domains.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.