Research project title
Accelerating Digital Twin Construction: A Domain Knowledge-driven, Low-Code Approach
Education level
Master or doctorate
Director/co-director
Director: Bentley Oakes
End of display
September 1, 2024
Areas of expertise
Modelling and simulation studies
Virtual reality and related simulations
Unit(s) and department(s)
Department of Computer Engineering and Software Engineering
Conditions
- Motivation to learn and solve problems
- Ability to communicate and write in English
Please see my students page for details on applying.
Detailed description
Digital Twins (DTs) are digital replicas of complex systems, such as machinery, factories, or even entire cities. Through advanced sensing, modelling, simulation, and precise control, their uses include scenario prediction, warnings of upcoming danger, and optimization for improved performance or lowered resource consumption. DTs are expected to reshape business and society by 2035 with a global market value exceeding C$64 billion within the next few years. Thus, investing in constructing DTs is important for Canada's industry, government, and citizens to understand and control their complex systems, especially in the manufacturing and infrastructure domains.
Despite the promising economic, societal, and environmental impact of DTs, current research has not yet addressed major DT construction barriers. Primarily, most users have disjointed and inconsistent domain knowledge (DK) such as system requirements, simulators, and models, with insufficient quality to construct a robust DT. Also, existing DT methodologies and tools are not suited for those without modelling experience, and they provide little guidance or recommendations. The user takes more time and expends more cognitive effort on this ad-hoc process than is necessary. This research program aims to address these barriers and significantly reduce the DT creation time and effort, such that users can assemble an initial DT for their system and begin leveraging services such as visualization and verification within a working week.
The first objective of this interdisciplinary research program is to provide a semantic (ontologically-based) structure and methodology for users to collect a sufficient amount of DK for constructing the DT. We will define DK quality metrics to guide users in making improvements.
Second, our research will produce a user-friendly, "low-code" open-source platform that integrates a step-by-step scaffolding methodology to construct DTs. The structured DK will provide the knowledge base to recommend suitable DT components, guided by the user's requirements for the DT.
The last research objective concerns improving the usability of advanced techniques such as verification and validation, and machine learning within the DT. These techniques increase system safety and optimize system behaviour, but their configuration and results are complex. To address this, we will provide (semi-) automated configuration of these components based on the DK provided. Technique results will be explained using the DK, providing the user with easy-to-understand counter-examples and visualizations.
