Complexity data science: A spin-off from digital twins

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.