Abstract
Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper, we explore how the concept of digital twins can be generalized into a broader, overarching field. From a theoretical standpoint, this generalization is achieved by recognizing that the duality of a digital twin fundamentally connects complexity science with data science, leading to the emergence of complexity data science as a synthesis of the two. We examine the broader implications of this field, including its historical roots, challenges, and opportunities.
Original language | English |
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Article number | pgae456 |
Pages (from-to) | 1-7 |
Number of pages | 7 |
Journal | PNAS Nexus |
Volume | 3 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- complexity science
- data science
- digital twin
- learning
- simulation