Digital Twin: Values, Challenges and Enablers

TL;DR

This paper reviews the current state, challenges, and enabling technologies of digital twins, emphasizing their transformative potential in designing and operating complex cyber-physical systems across various industries.

Contribution

It provides a comprehensive overview of recent methodologies, challenges, and technological enablers for constructing digital twins, offering insights for stakeholders.

Findings

Digital twins are increasingly important in cyber-physical systems.

Recent advances enable more accurate and adaptive digital twin models.

Challenges include data management, model fidelity, and integration issues.

Abstract

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.