Digital twin technology has significant promise, relevance and potential of\nwidespread applicability in various industrial sectors such as aerospace,\ninfrastructure and automotive. However, the adoption of this technology has\nbeen slower due to the lack of clarity for specific applications. A discrete\ndamped dynamic system is used in this paper to explore the concept of a digital\ntwin. As digital twins are also expected to exploit data and computational\nmethods, there is a compelling case for the use of surrogate models in this\ncontext. Motivated by this synergy, we have explored the possibility of using\nsurrogate models within the digital twin technology. In particular, the use of\nGaussian process (GP) emulator within the digital twin technology is explored.\nGP has the inherent capability of addressing noise and sparse data and hence,\nmakes a compelling case to be used within the digital twin framework. Cases\ninvolving stiffness variation and mass variation are considered, individually\nand jointly along with different levels of noise and sparsity in data. Our\nnumerical simulation results clearly demonstrate that surrogate models such as\nGP emulators have the potential to be an effective tool for the development of\ndigital twins. Aspects related to data quality and sampling rate are analysed.\nKey concepts introduced in this paper are summarised and ideas for urgent\nfuture research needs are proposed.\n