ANANKE

In recent years, the concept of the digital twin has been increasingly used in science and engineering to describe the challenge of developing a numerical avatar of an intended or actual real physical product, system or process.  While model-driven approaches based on equations modeling the physics of interest were initially predominant, very effective data-driven approaches are now becoming increasingly popular. However, both approaches should not be played off against each other, as they complement each other. In fact, we believe that digital twin must combine (1) modeling and simulation to create a virtual representation of a physical counterpart mostly based on physical principles and (2) a bidirectional interaction between the virtual and the physical. This bidirectional interaction forms a feedback loop that comprises dynamic data-driven model updating (e.g., sensor fusion, inversion, data assimilation) and optimal decision-making (e.g., control, sensor steering)}.

In this context, the objective of the Ananke team-project is to formulate and analyze methods for integrating multimodal information sources into causal dynamic physical models with the aim of prediction and control. Our main focus will be dynamical systems modeled by partial differential equations. This framework will cover the mathematical and methodological foundations up to the real applications in different contexts: life sciences, environmental sciences or engineering.

We follow a model-driven vision, where we believe that the general concept of digital twin corresponds to a mathematical multimodal coupling between information sources in the same abstraction, as the multiscale coupling is seen as the coupling between physical scales or the multiphysical coupling is understood as the coupling between different physics. And the physical model description is central to unify the different sources of information. In other words, the model should be understood as a common language for the integration of data.