Complexity data science : A spin-off from digital twins

Frank Emmert-Streib*, Hocine Cherifi, Kimmo Kaski, Stuart Kauffman, Olli Yli-Harja

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 languageEnglish
Article numberpgae456
Pages (from-to)1-7
Number of pages7
JournalPNAS Nexus
Volume3
Issue number11
DOIs
Publication statusPublished - 1 Nov 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • complexity science
  • data science
  • digital twin
  • learning
  • simulation

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