Composing DTs with Variability, Fidelity and Uncertainty

Context

Digital twins are virtual representations of real-world products, systems, or processes, enabling simulation, integration, testing, monitoring, and maintenance. They play a pivotal role in optimizing complex systems across a wide range of domains, from industrial manufacturing and energy to environmental monitoring and healthcare.

The Engineering Digital Twin EDT program, funded by the France 2030 investment plan, is a national initiative aimed at advancing the foundations of digital twin engineering in France and Europe. By bringing together leading academic and industrial partners, EDT seeks to strengthen the bases for the design, use, and deployment of digital twins, addressing key open challenges in model hybridization, composability, development methodologies, digital coupling, and human–twin interaction.

The challenges addressed in this thesis are related to the open question of how DTs could be modularized to allow their composition either at design time or at deployment time, leveraging the notion of contract [3] for their provided and required interfaces, including the management of Variability, Fidelity and Uncertainty. This modularisation also concerns the services provided by the DTs, including data processing and what-if exploration based on eg Machine Learning.

While a model is always “wrong” with respect to reality, some models can be helpful, provided we know about the distance they have with reality. This goes along three dimensions: scale, fidelity and uncertainty management [2]. Scale and Fidelity can be understood as the level of abstraction of the DT (the scale of the map), whereas uncertainty management is the confidence we have in the DT attributes values (eg the river’s width is 10m ± 2m).

Thesis Objectives

This PhD project aims to propose concepts, methods and tools to allow the composition of digital twins components with an explicit handling of Variability, Fidelity and Uncertainty. Key scientific challenges include:

• [Propose a meta-model and an architecture to allow the composition of digital twins]
• [Handle variability modeling as well as automated variability realization, based on UVL and Greal [4]]
• [Handle scale, fidelity and uncertainty management]

The results of this thesis will directly contribute to the Artemis platform, an open-source framework set to become a benchmark in the field.

Work Environment

The PhD candidate will be co-supervised by Jean-Marc Jézéquel and Benoit Combemale, IRISA/DiverSE, as well as Antoine Beugnard, P4S/IMT-Atlantique within the DiverSE team (https://www.diverse-team.fr/) at IRISA, University of Rennes, the best French university in Computer Science (according to ARWU 2025 ranking). The candidate will benefit from a stimulating scientific and industrial environment of the highest level, with access to a national network of leading research institutions and industry partners, regular interactions with the broader EDT community through workshops, seminars, and joint demonstrators, and the opportunity to contribute to Artemis, the program’s open software platform.

What You Will Gain from This PhD

This PhD offers the opportunity to:

• Develop highly sought-after skills in system modeling, real-time data processing, and collaborative innovation.
• Collaborate with leading partners (Inria, CEA, CNRS, etc.) and validate your research on real-world industrial use cases.
• Join a network of PhD candidates within the EDT program, fostering collaboration, peer support, and interdisciplinary exchanges.
• Contribute to an open-source platform (Artemis) and publish in international conferences and journals.
• Gain recognition in a rapidly growing field, with career prospects in academic research, industrial R&D, or entrepreneurship.

Upon completion, you will be positioned as a recognized expert in a key domain for industry and research, with diverse professional opportunities in France and internationally.

References

• Benoît Combemale, Pascale Vicat-Blanc, Arnaud Blouin, Hind Bril El Haouzi, Jean-Michel Bruel, et al.. Engineering Digital Twins: A Research Roadmap. EDTconf 2025 - 2nd International Conference on Engineering Digital Twins, Oct 2025, Grand Rapids, Michigan, United States. pp.1-7.

• Simona Bernardi, Michalis Famelis, Jean-Marc Jézéquel, Raffaela Mirandola, Diego Perez Palacin, Fiona Polack, and Catia Trubiani. Living with Uncertainty in Model-Based Development, pages 159–185. Springer International Publishing, July 2021.

• Antoine Beugnard, Jean-Marc Jézéquel, Noël Plouzeau, and Damien Watkins. Making components contract aware. Computer, 32(7):38–45, July

• Experience in Specializing a Generic Realization Language for SPL Engineering at Airbus Damien Foures, Mathieu Acher, Olivier Barais, Benoit Combemale, Jean-Marc Jézéquel, Jörg Kienzle MODELS 2023 - 26th International Conference on Model-Driven Engineering Languages and Systems, ACM; IEEE, Oct 2023, Västerås, Sweden. pp.1-12