Digital Twinning Network
Digital twins are digital replicas of living, physical, or social systems that are updated with real-time data and can be used to analyse and predict their behaviour through simulation. They provide organisations with the capability to conduct experiments and anticipate future events, enabling better design, planning, and monitoring of complex systems.
Digital twinning research involves a range of different technologies and methodologies: building mathematical models of complex systems, setting up computational simulation environments, designing new sensors for data capture in the physical environment, applying machine learning to derive insight from unstructured data, using virtual and augmented reality systems to allow users to explore simulated worlds, and developing optimisation methods to provide decision support to stakeholders.
Researchers at the University of Edinburgh’ Digital Twinning Network apply these techniques to a range of different use cases in high-impact application domains, such as:
- Construction, building, and infrastructure maintenance and repair
- Human-robot management of manufacturing environments
- Waste management and the circular economy
- High-precision simulation of turbine engines
- Mobility and transportation in urban environments
- Energy system planning in the national grid
- Large-scale modelling of geophysical environments
- Modelling human tissue and organs
- Simulating animal genetic, breeding, and food systems
- Optimising the delivery of health and social care
- Understanding the dynamics of financial and economic systems
Our network of over twenty academic experts leading digital twinning initiatives combines research excellence in the computational, engineering, mathematical and social sciences to create new exciting opportunities and facilitate collaboration with external partners.
While domain-specific solutions require highly specialised approaches, at Edinburgh we emphasise combining different data sources across domains (e.g. health and finance, geospatial and biomedical), innovating through next-generation technologies (e.g. robotics, supercomputing, virtual reality), and understanding the value digital twins offer to human users and organisations.
We welcome new members from across the University of Edinburgh, so if you are interested in joining the network, then please contact Professor Michael Rovatsos.
Selected researcher profiles:
|Researcher||Area of Expertise|
Climate smart agriculture, circular nutrient economy, soil biogeochemistry, mass spectrometry, microbial processes underpinning greenhouse gas production and mitigation, decision support systems for agriculture in sub Saharan Africa
|Infrastructure and built environment, construction informatics, building information modelling, 2D/3D data acquisition and processing, machine learning, VR and AR, project management, lean construction, heritage.|
|Chris Dent||Decision support for infrastructure systems and government, uncertainty management and calibration of models, capital planning and project/policy appraisals, infrastructure system operation, energy security of supply|
|Dimitrios Gerogiorgis||First-principles and data-driven modelling, design and optimisation of chemical, (bio)pharmaceutical, food and drink, and fuel systems.|
|Daniel Goldberg||First-principles and data-driven modelling, design and optimisation of chemical, (bio)pharmaceutical, food and drink, and fuel systems.|
|Gregor Gorjanc||Managing and improving agricultural populations using data science, genetics and breeding (we regularly build digital twins of agricultural populations)|
Remote Sensing, Earth Observation, Altimetry, Radar, Satellites, Glaciers, Ice Sheets, Sea Level Change, Climate, Digital Twin of Antarctica, European Space Agency, Industry, Start-up, Automatisation, Data fusion
|Ewa Luger||Human Computer Interaction, Design, Ethics and Governance, Consent, Social Policy, Algorithmic|
|Mark Parsons||Supercomputing for the largest modelling, simulation and AI problems; digital twins for large-scale engineering challenges; converged infrastructure for data science and HPC, large-scale AI training infrastructure; industry collaboration|
|Roberto Rossi||Decision making under uncertainty, automated reasoning, stochastic programming, supply chain & inventory analytics|
|Michael Rovatsos||distributed AI, agent-based modelling, modelling socioeconomic and organisational systems, decentralised architectures for integrating data-driven models, logistics and transportation|
|Geoff Simm||Food systems sustainability; food systems and One Health interactions|
|Chris Speed||Design Informatics, interaction design, human-centred design, Internet of Things, decentralised and distributed systems, blockchain, digital economics, co-design|
|Adam A. Stokes||
Bioinspired engineering, industrial robotics, soft robotics, fluidics, complexity, multidisciplinary research on soft systems engineering, and entrepreneurship.
|Amos Storkey||Machine Learning, Deep Learning and Artificial Intelligence methods. Data driven learning and inference for simulation and model development in the earth sciences.|
|Carolina Toczycka||Governance and accountability for digitization, infrastructure project delivery, performance and benefit evaluation, policy implementation|
Multimodal AI, semi and weakly supervised learning, representation learning, healthcare AI, computer vision
Model-data fusion, terrestrial ecosystem and biogeochemical modelling, forest and crop dynamics, assimilation of earth observation data
- A construction-phase digital twin model
- A tool for building stochastic simulations of breeding programmes
- Advanced Simulation and Modelling of Virtual Systems
- Centre for the Decentralised Digital Economy
- A digital twin of Antarctica
- High-resolution interfacing with the nervous system
- Modelling and optimisation of carbon bed systems for the pharma industry
- Providing structure in unstructured extreme environments