AITwin: A Uniform Digital Twin Interface for Artificial Intelligence Applications

Cyber-physical systems that integrate machine learning (ML)-based services and methods from the broader field of Artificial Intelligence (AI) rely on a virtual representation of the underlying real physical system. Unfortunately, depending on respective solution approaches, usually similar but rarely the same virtual representation of the physical system is required. Thus, two solutions for the same problem might use different virtual representations. Informed Machine Learning is one technique to integrate expert knowledge into AI applications. It uses techniques to combine an often proprietary and expert-defined virtual representation with data from a real cyber-physical system. But methods for Informed ML have a much higher demand on the virtual representation than, for example, traditional distance-based methods in Machine Learning. Informed ML requires domain specific knowledge, which needs to be represented in some standardized Digital Twin as its virtual representation. Practitioners benefit through some categorization indicating which Digital Twin can be used to acquire a unique virtual representation of a cyber-physical system. Especially, by using a common standardized application programming interface (API). In short: a standardized Digital Twin is needed for AI-based solutions. In this chapter, such an API for Digital Twins for AI solutions is presented and different levels of complexity for Digital Twins are defined. The suggested API is considered as an AI reference model and is verified by using it on several simulated and real examples from the process and manufacturing industries. Additionally, it is compared against currently ongoing research projects.