Federation in Digital Twins and Knowledge Transfer: Modeling Limitations and Enhancement

Abstract

Digital twins (DTs) consist of various technologies and therefore require a wide range of data. However, many businesses often face challenges in providing sufficient data due to technical limitations or business constraints. This can result in inadequate data for training or calibrating the models used within a digital twin. This paper aims to explore how knowledge can be generated from federated digital twins—an approach that lies between digital twin networks and collaborative manufacturing—and how this can be used to enhance understanding for both AI systems and humans. Inspired by the concept of federated machine learning, where data and algorithms are shared across different stakeholders, this idea involves different companies collaborating through their respective DTs, a situation which can be referred to as federated twinning. As a result, the models within these DTs can be enriched with more-detailed information, leading to the creation of verified, high-fidelity models. Human involvement is also emphasized, particularly in the transfer of knowledge. This can be applied to the modeling process itself, which is the primary focus here, or to any control design aspect. Specifically, the paradigm of thermal process modeling is used to illustrate how federated digital twins can help refine underlying models. Two sequential cases are considered: the first one is used to study the type of knowledge that is required from modeling and federation; while the second one investigates the creation of a more suitable form of modeling.