Towards ecosystems of connected digital twins to address global challenges

A digital twin is a virtual representation of a physical entity. By bringing together data, modelling and simulation, digital twins enable insights about the performance of structures, products and processes. They are powerful tools for understanding and optimising complex systems that are already beginning to demonstrate their potential across multiple sectors, from engineering and manufacturing to health and the environment.

In this white paper, we share learnings from The Alan Turing Institute’s AI for science and government (ASG) programme, which has been conducting foundational research and applied work in digital twins under the ‘Ecosystems of digital twins’ (EDT) theme. ASG researchers are expanding the scale and complexity, and deepening the application and deployment, of digital twins.

We organise digital twins into three groups according to their level of complexity, scaling up from a digital twin of a single asset or system, to a digital twin incorporating multiple assets or systems. At the third level, digital twins of differing levels of complexity are linked together in an ‘ecosystem of digital twins’. We provide case studies demonstrating the EDT theme’s multidisciplinary work on digital twins for each of these three levels.

Through digital twins, we can develop unprecedented capabilities for understanding, manipulating and managing complex systems. Within the EDT theme, we have found that for the full promise of the technology to be realised, we must consider the development and deployment of digital twins at the ecosystem-level. By connecting together digital twins within a virtual environment, we can enable sharing of information between ‘federated’ assets – assets under different jurisdictions and restrictions – unlocking more advanced insights and capabilities than are possible at the lower levels. Within an ecosystem, connected digital twins can also promote more varied applications, for example, by combining representations of, and insights from, both physical and social systems to support decision-making.

There are, however, certain technical, social and institutional issues that need to be negotiated in developing ecosystems of digital twins. We therefore provide the following three recommendations in support of the progress which needs to be made:

1. Elevate cross-disciplinary activities and spaces for digital twins.
2. Invest in open infrastructure, with a focus on data and technical standards.
3. Prioritise tools for building trust in and understanding of digital twins.

By following these recommendations, with support from the right investments, we can build robust ecosystems of digital twins capable of helping us tackle some of the most important global challenges, from pandemics to adapting to climate change.