  Digital twin technology has significant promise, relevance and potential of
widespread applicability in various industrial sectors such as aerospace,
infrastructure and automotive. However, the adoption of this technology has
been slower due to the lack of clarity for specific applications. A discrete
damped dynamic system is used in this paper to explore the concept of a digital
twin. As digital twins are also expected to exploit data and computational
methods, there is a compelling case for the use of surrogate models in this
context. Motivated by this synergy, we have explored the possibility of using
surrogate models within the digital twin technology. In particular, the use of
Gaussian process (GP) emulator within the digital twin technology is explored.
GP has the inherent capability of addressing noise and sparse data and hence,
makes a compelling case to be used within the digital twin framework. Cases
involving stiffness variation and mass variation are considered, individually
and jointly along with different levels of noise and sparsity in data. Our
numerical simulation results clearly demonstrate that surrogate models such as
GP emulators have the potential to be an effective tool for the development of
digital twins. Aspects related to data quality and sampling rate are analysed.
Key concepts introduced in this paper are summarised and ideas for urgent
future research needs are proposed.
