Digital Twins: A Computational Realization of the Scientific Method in Dynamical Systems

Abstract

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.