The scientific method is widely acknowledged as an authoritative framework that provides guiding principles for empirical research across disciplines. Despite this central role, it is rarely examined explicitly as a conceptual framework. In this paper, we revive attention to its role by revealing a connection to digital twins, which have received considerable attention in recent years. Specifically, we argue that the digital twins framework can be interpreted as a computational realization of the scientific method in the context of dynamical systems. This connection is rooted in the dynamical nature of models, since dynamical systems arise across many scientific fields, from physics to economics, and also constitute a core component of digital twins. The main benefits of this connection include a common scientific language for knowledge transfer, a systematic approach that emphasizes the mechanisms of continuous learning and model selection, and a practical framework for implementing the scientific method computationally across disciplines.
The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts ha…
Digital twins (DTs) have become a central paradigm for modeling cyber–physical systems and digital infrastructures, yet the term is applied to very different representations—from physical assets to operational processes and service environments. This ambiguity obscures how the va…
Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to over…
Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent and interpretable explanations…
Recent work on decentralized computational trust models for open multi-agent systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee’s perspective. This new model addresses a serious unresolved problem in existing trust and reputa…
Bioinspired computing methods, such as Artificial Neural Networks (ANNs), play a significant role in machine learning. This is particularly evident in smart manufacturing, where ANNs and their derivatives, like deep learning, are widely used for pattern recognition and adaptive c…