Building a framework for the design and deployment of digital twins: the Digital Twin Factory project

The digital twin is a useful tool for scientists and decision makers to understand the present (what now), explore future trajectories (what next) to to investigate the future impacts of current risk mitigation actions (what if), or of a system. Working at the local scale allows detailed physics to be implemented in an approach that better captures the complexity of the study site (city, watershed, etc.) in an approach that complements the global scale. The availability of very high-precision spatial products (optical, 3D, thermal, etc.) enables this high-precision local analysis anywhere on the Earth.This growing interest is leading a number of actors to build digital twins at the local scale. However, building this type of representation requires a dedicated effort from the user, usually a scientist, which prevents him from focusing on the scientific added value he could bring with his thematic expertise.
The Digital Twin Factory (DTF, 2024-2026) project, coordinated by the French National Centre of Space Studies (CNES), aims to provide users with a framework capable of building, deploying and operating a digital twin at the scale of the site. It is designed as a Digital Twin as a Service API (PaaS) to abstract the underlying infrastructure, with possibility of accessing both the HPC resources and usual Cloud providers. The DTF also provides users with methodological building blocks to access (catalogue harvester), manipulate (ingester, data processing pipeline), visualize and analyze (plot, dashboarding) the data. In this way, the instantiators of the digital twin can focus on their thematic expertise and deploy their physical solvers with access to multi-source data.
While high performance computing resources can be made available to run these physical models, parametric studies or climate trajectories may require high cost and long simulation times. Partial or full data based surrogate model is an approach that can overcome this barrier and provide results in a reactive manner. Part of the DTF's work is therefore aimed at providing users with methodological building blocks for surrogate modelling, based on the expertise of the scientific community.
This contribution presents the multi-layered architecture of the DTF project, its different components and the services offered to users. We illustrate this work with the construction of the digital Twin of Nokoué Lake in Benin that integrates flood forecasting, pollution control, salinity management, long-term risk evolution, risk governance, and adaptation measures. Satellite data are used as input for a hydrodynamic code, on which first developments of surrogate models are presented.