Complexity data science: A spin-off from digital twins.

Publication Title

PNAS Nexus

Document Type

Article

Publication Date

11-1-2024

Keywords

washington; isb

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.

Area of Special Interest

Cardiovascular (Heart)

Area of Special Interest

Cancer

Specialty/Research Institute

Cardiology

Specialty/Research Institute

Oncology

DOI

10.1093/pnasnexus/pgae456

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