Research themes

Natural environment

The Natural Environment theme’s aim is to develop cutting edge DT technology to fully exploit environmental data, monitoring, and modelling expertise. In doing so, we aim to provide a step change in our understanding of environmental change and prediction abilities to support sustainable interactions with our environment and to provide the insights that are crucial for climate change adaptation and mitigation.

Working together with an established network of partners, the theme (and wider TRIC-DT community) will establish the UK as the global leader in the development of environmental digital twins and ensure that they are firmly embedded into national plans for cyber-physical infrastructure and play their full role in providing data and information for decision-making.
 

Infrastructure

The infrastructure theme’s aim will be to develop underpinning technologies in support of DT design, validation, and implementation, and to demonstrate them in the context of critical infrastructure.

Fundamental research will focus on the key components of DTs, expanding theory and practice in terms of system uncertainty analysis, computer model validation via experiment, and the transformation of machine-learning decision support from a probability-of-error viewpoint to one based on risk and utility.

An important objective will be to establish methods which allow the DT to evolve in time in correspondence with the physical twin. The research will be challenging as it will need to address the analysis of markedly nonlinear and nonstationary systems and structures. This will include extend the fundamental theory to encompass populations of structures and ecosystems of digital twins, and developing methods of increasing the safety, longevity and resilience of critical infrastructure.

Ultimately, we intend to create demonstrators based on real-world full-scale structures from a range of engineering disciplines, encompassing transport, civil, offshore energy and aerospace. For example, one important application domain will be that of health monitoring of civil infrastructure; using digital twins combined with powerful state-of-the-art machine-learning methods.

Health

The Health theme’s aim is to develop state-of-the-art tools, innovative methods, and impactful demonstrations that enable the creation of robust and rapid digital twins at scale. We will initially limit the scope to cardiac applications to provide focus and common links between tasks.

We will develop a comprehensive set of open-source tools for rapid anatomical and functional cardiac digital twin development. To demonstrate the scalability of these we will apply the workflow to the large numbers of data sets available through UK Biobank.

We will prototype a patient-specific clinical service that monitors and predicts patient outcomes following cardiac diagnosis and therapy. This personalized service will leverage the power of digital twin technology, providing accurate and timely predictions to enhance patient care and improve treatment outcomes.

These application tasks will be enabled through the use of emulators. To overcome barriers and challenges in evaluating, comparing, and utilising emulators for creating and analysing computational models, we will dedicate our efforts to the development of open-source libraries. These libraries will significantly reduce barriers, facilitating the seamless evaluation, comparison, and usage of emulators, thereby fostering collaboration among researchers and practitioners in computational modelling.

Transparency and reproducibility are vital to our mission. We will publish transparent and reproducible digital twin processes, working closely with regulators to establish credibility standards. Through this collaborative effort, we aim to demonstrate the immense clinical value of digital twin approaches in healthcare, thereby increasing access and reducing barriers to wider adoption.

Innovation and Impact Hub

The Innovation and Impact Hub (TRIC-DT Hub) will help coordinate and convene the scientific aims and objectives of the three themes, by supporting the development of open and reproducible tools, driving impact with diverse stakeholders, building and supporting multi-disciplinary communities of practice, and embedding best practices for responsible research and innovation throughout the TRIC-DT.

This work will be undertaken alongside focussed understanding and evaluation of needs and challenges faced by the TRIC-DT communities (e.g. internal and external stakeholder engagement, participatory design workshops for research infrastructure). Leveraging the expertise of dedicated research community managers and research application managers, the TRIC-DT Hub will also ensure that the scientific vision of the three themes is aligned with the challenge-led focus of Turing 2.0 ambitions for science and innovation by leveraging existing tools and practices from across the Turing and its network (e.g. open and reproducible guidance on best practices from TPS programme).

The Hub team will work with the TRIC-DT to develop robust and reliable indicators to measure progress against the Hub’s own aims and the wider impact of the TRIC-DT research. For instance, indicators that demonstrate effective validation and impact of open and community-driven research infrastructure.