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 |
|---|---|
| Article number | pgae456 |
| Number of pages | 7 |
| Journal | PNAS Nexus |
| Volume | 3 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2024 |
| Publication type | A1 Journal article-refereed |
Funding
This work was supported by the Academy of Finland (352266 to F.E.-S. and 352263 to O.Y.-H.). The funders had no role in study design, data collection and analysis, publication decision, or manuscript preparation.
| Funders | Funder number |
|---|---|
| Research Council of Finland | 352263, 352266 |
| Research Council of Finland |
Keywords
- complexity science
- data science
- digital twin
- learning
- simulation
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- General
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