High-tech systems are increasingly complex and difficult to design, produce and maintain. There is a lack of synergy between model-based engineering and data-driven approaches, which hamper the development of reliable and agile digital twins. Besides, data analytics is hardly used in real-time decision-making due to the lack of guarantees on performance and safety. A digital twin is the description of a process or system that is enhanced with (live) data simulating the real world as accurately as possible. Researchers aim to develop methods to make accurate digital twins of such high tech systems; with the development of a unified mathematical (mathware) and ICT (software) platform for integrating model-based engineering methods and data-driven approaches to significantly improve the current value chain. This asks for a more integrated approach in which the identification and control steps are closely intertwined and models are updated via data-driven methods. The challenge for this research is to establish a more engineering discipline instead of developing one-off solutions.

For applied mathematics and computational sciences to develop the mathematical underpinnings and scalable algorithms that will take such a digital twin to the next level. This means moving from doing the fundamental science on how to build and set up a digital twin to becoming an engineering tool that can be used for designing and controlling high tech applications. The researchers aim to develop a platform that covers all key enabling technologies for the automated development of digital twins and their use for technology health management, as well as for control systems reconfiguration and optimization, and demonstrating this in a real-life setting.