Abstract
This paper presents a literature review on methods for enabling real-time analysis in digital twins, which are virtual models of physical systems. The advantages of digital twins are numerous, including cost reduction, risk mitigation, efficiency enhancement, and decision-making support. However, their implementation faces challenges such as the need for real-time data analysis, resource limitations, and data uncertainty. The paper focuses on methods for reducing computational demands, which have not been systematically discussed in the literature. The paper reviews and categorizes methods and tools for accelerating the modeling of physical phenomena and reducing the computational needs of digital twins.
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