Defining a Digital Twin: A Data Science-Based Unification

Abstract

The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine.

Increasing acceptance of AI‐generated digital twins through clinical trial applications

“…While DT technology has been embraced by the fields of engineering and manufacturing for decades, 5 the concept of employing DTs in healthcare has only recently gained traction. In general, a DT is defined as “a mathematical model with an updating mechanism that generates data which are indistinguishable from its physical counterpart.” 5 This model of a real‐world entity permits virtual interactions that support forecasting, experimentation, and simulation involving that entity. In cardiology, DTs of the heart have been used to examine in silico organ‐drug interactions (Dassault's Living Heart) and plan cardiac procedures (FEops HEARTguide).…”

A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems

“…Accelerated innovation: The synergy between digital models, simulation tools, and adaptive manufacturing fosters a culture of innovation. This accelerates the innovation cycle, allowing companies to introduce innovative products and processes with greater speed and confidence [15][16][17].…”