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Digital twins, virtual models combining simulation and learning, are generalized into complexity data science. This new field synthesizes complexity and data science, offering broad implications and opportunities.

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Area of Science:

  • Multidisciplinary science
  • Data science
  • Complexity science

Background:

  • Digital twins are gaining traction in medicine (oncology, immunology, cardiology).
  • The core concept involves creating a virtual model of a physical entity through simulation and learning.
  • Existing applications highlight the potential but lack a generalized theoretical framework.

Purpose of the Study:

  • To generalize the concept of digital twins into a broader scientific field.
  • To establish the theoretical underpinnings connecting digital twins to complexity and data science.
  • To explore the implications, history, challenges, and opportunities of this generalized field.

Main Methods:

  • Theoretical generalization of the digital twin concept.
  • Identifying the synthesis of complexity science and data science.
  • Analyzing the interdisciplinary connections and foundational principles.

Main Results:

  • The generalization of digital twins leads to the emergence of 'complexity data science'.
  • This synthesis unifies principles from complexity science and data science.
  • The paper provides a foundational framework for this emerging field.

Conclusions:

  • Complexity data science offers a unified approach to understanding and developing advanced digital twin applications.
  • This generalized field has significant potential for future research and practical implementation across various domains.
  • Recognizing the duality of digital twins opens new avenues for interdisciplinary scientific inquiry.