Francisco Chinesta

Abstract:

In the previous industrial revolution, virtual twins (emulating a physical system) were major protagonists. Usually, numerical models (virtual twins), however, are static, that is, they are used in the design of complex systems and their components, but they are not expected to accommodate or assimilate data. The reason is that the characteristic time of standard simulation strategies is not compatible with the real-time constraints which are mandatory for control purposes. Model Order Reduction techniques opened new possibilities for more efficient simulations.
The next generation of twins, the so-called digital twins, allowed for assimilating data collected from sensors with the main aim of identifying parameters involved in the model as well as their time evolution in real time, anticipating actions using their predictive capabilities. Thus, simulation-based control was envisaged and successfully accomplished in many applications. Despite an initial euphoric and jubilant period, unexpected difficulties appeared immediately. Namely, in practice, significant deviations between the predicted and observed responses were noticed, limiting or abandoning their use in many applications.
In that framework of multi-uncertainty evolving environments, Hybrid Twins we proposed, consisting of three main ingredients: (i) a simulation core able to solve complex mathematical problems representing physical models under real-time constraints, (ii) advanced strategies able to proceed with data-assimilation, data-curation, data-driven modelling and finally data-fusion when using compatible descriptions for the physical and data-based models, and (iii) a mechanism to adapt the model online to evolving environments (control).

Virtual, Digital and Hybrid Twins. A new paradigm in data-based engineering and engineered data. F. Chinesta, E. Cueto, E. Abisset, J.L. Duval, F. El Khaldi. Archives of Computational Methods in Engineering, https:\doi.org10.1007s11831-018-9301-4, 2018.