DIGITAL TWIN MODELLING VIA INTEGRATION OF SIMULATION AND DATA-DRIVEN METHODS

Digital twins – high-fidelity digital counterparts of physical assets – are increasingly used to solve real-world problems across industries. Building a high-quality digital twin requires an integrated stack spanning IoT, data processing, modelling & simulation, 3D visualisation, and networking, with the modelling layer pivotal. Yet widely adopted modelling practices remain limited. We propose a digital twin modelling method that combines simulation and data-driven modelling, selecting among three integration strategies by goal: (i) accuracy enhancement via calibration, assimilation, and hybridisation; (ii) execution efficiency via surrogate or reduced-order models; and (iii) decision optimisation via simulation-in-the-loop using learned response surfaces. We formalise selection criteria and workflows for each strategy and show their composition within a single methodology. A smart farm case study demonstrates improved predictive accuracy, reduced runtime, and support for operational optimisation, illustrating practical value for purpose-built digital twins.