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As an emerging technology in the era of Industry 4.0, digital twin is gainingunprecedented attention because of its promise to further optimize processdesign, quality control, health monitoring, decision and policy making, andmore, by comprehensively modeling the physical world as a group ofinterconnected digital models. In a two-part series of papers, we examine thefundamental role of different modeling techniques, twinning enablingtechnologies, and uncertainty quantification and optimization methods commonlyused in digital twins. This second paper presents a literature review of keyenabling technologies of digital twins, with an emphasis on uncertaintyquantification, optimization methods, open source datasets and tools, majorfindings, challenges, and future directions. Discussions focus on currentmethods of uncertainty quantification and optimization and how they are appliedin different dimensions of a digital twin. Additionally, this paper presents acase study where a battery digital twin is constructed and tested to illustratesome of the modeling and twinning methods reviewed in this two-part review.Code and preprocessed data for generating all the results and figures presentedin the case study are available on GitHub.

A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives