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Academic Thread
February 7 - 9, 2024

Throughout the three streams of the conference, academic presentations relevant to the thread and immediate topic will be included.

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Leaders in the world’s Digital Twins sector are coming together from Aus/NZ, the United Kingdom, and North America to engage and collaborate by sharing their thought leadership on the Digital Twins revolution. The Academic Thread of the Digital Twins conference runs throughout the conference on each of the 3 streams, with designated paper presentations and posters.


Digital Twinning is a vast, multidisciplinary domain with broad applications. Digital Twins enable the next generation of analysis interconnecting various systems with a high degree of complexity. They benefit from historical, concurrent, and predictive analytics, as well as numerous subfields of simulation. Digital Twins also benefit from a variety of scientific fields as core components but also as part of the phenomena they aim to simulate.

We are welcoming paper submissions from all allied disciplines.


This inaugural Digital Twins conference will be themed around (A) Transforming Finance with Digital Twins, (B) Connected Digital Twins and Social-Technical Solutions, and (C) Advancing Digital Twins Governance.  Submissions with relevance (theoretical or applied) are prioritized for paper presentations. These papers can cross paths with smart cities, urban environments, and the intersection of Digital Twins with human health and with climate, specifically, designing and building scalable, interoperable, and reusable Digital Twins for sustainable cities and resilient infrastructure.


The Digital Twins conference will bring together leading researchers and practitioners with diverse, yet highly integrated, scientific and engineering backgrounds.

The core topics include:

  • Transforming Finance with Digital Twins: Exploring the Opportunities and Challenges

  • Connected Digital Twins and Social-Technical Solutions

  • Advancing Digital Twins Governance: Best Practices and Standards

  • Foundational Models

  • Privacy Preserving

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