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Workshop on Model-based System Engineering and Artificial Intelligence - MBSE-AI Integration 2024

21 - 23 February, 2024 - Rome, Italy

In conjunction with the 12th International Conference on Model-Based Software and Systems Engineering - MODELSWARD 2024


Afef Awadid
Technological Research Institute SystemX
Brief Bio
Afef Awadid is a researcher in systems and process engineering at the Technological Research Institute SystemX, where she has contributed to several joint R&D projects. As one impact of the digital transformation on industry is an increasing automation of business processes and the accompanying need for understanding their complexity, her research activities in these projects have largely focused on how to master such complexity using model-centric approaches. In 2019, she received a PhD in computer science from the University of Paris 1 Pantheon-Sorbonne, where she served as a teaching assistant for two years. Her research interests include systems engineering, system architecture, Artificial Intelligence (AI)-based systems, decision-making, model-based approaches, and process engineering.
Olivia Penas
Technological Research Institute SystemX
Brief Bio
Dr. Olivia Penas received in 1999 the M.Sc. degree in Material Science Engineering, in 2002 the Ph.D. degree in Physics of Materials (Smart Space composites), from INSA Lyon (France), and in 2019 the HDR degree (French habilitation to supervise PhD). She was head of the research technical support teams of Supmeca in 2006, then Deputy Director of LISMMA Laboratory in 2013 and Research Deputy Director of ISAE-Supmeca (Paris) since 2015. From 2023, she is in charge of the Systems Engineering scientific axis at the IRT SystemX. Her main research topics are model-based systems engineering (MBSE), agility, mechatronic conceptual design, knowledge management and ontology, Agility, MBSE data consistency, formalization and interoperability.
Mohamed Ibn Khedher
Technological Research Institute SystemX
Brief Bio
Mohamed Ibn Khedher obtained his Ph.D. in computer science from Telecom SudParis in collaboration with the University of Evry, France. During his Ph.D., he developed software for person re-identification from video sequences. From 2015 to 2018, he worked as a software engineer at a research center specializing in Advanced Driver Assistance Systems. Since 2018, he has been working as a Research Engineer at IRT-SystemX, focusing on trustworthy AI, which encompasses adversarial machine learning, explainable AI, and formal verification of neural networks. His primary interests include neural network verification, biometrics, early detection of neurodegenerative diseases, and anomaly detection. In terms of his scientific activities, he has published over thirty scientific publications in international conferences and journals. He has supervised more than twenty interns, including master's and PFE students, and has been involved in the supervision of four doctoral theses.
Bertrand Braunschweig
Technological Research Institute SystemX
Brief Bio
Bertrand Braunschweig began his career in the oil industry as a researcher in systems dynamics and artificial intelligence. He then joined IFP Énergies Nouvelles to manage AI research and coordinate international interoperability standards projects. He spent five years at the ANR as head of the ICST department, before joining Inria in 2011 as director of the Rennes research centre, then of the Saclay research centre, and then to steer the research component of the national AI strategy. He is now an independent consultant and provides support to various organisations, notably as scientific coordinator of the programme operated by IRT SystemX. Bertrand Braunschweig is an ENSIIE engineer, PhD from Paris Dauphine University and Habilitation from Pierre and Marie Curie University.


AI-empowered MBSE (Model-based System Engineering)" is increasingly advocated as a promising approach to overcome MBSE challenges and to promote high-quality systems. At the same time, recent research projects such as the program "" advocate an MBSE-empowered AI as a way to build trustworthy AI systems.
This workshop will be the opportunity to discuss how to choose, apply, evaluate and adapt AI techniques to support MBSE and how to adopt an MBSE approach to produce high-quality and reliable AI systems. Thus, “AI for MBSE” and “MBSE for AI” are both in the scope of the proposed workshop.


Topics of interest include, but are not limited to:
  • AI for MBSE
  • AI techniques for traceability, consistency, and completeness of system engineering information
  • AI techniques to support the automated translation of NL requirements into semi-machine-readable requirements
  • AI trustworthiness assessment for MBSE of critical systems
  • AI-supported modelling (e.g., bots, recommenders, UI adaptation, etc.)
  • AI-supported requirements modeling
  • Application of (meta-heuristic) search and machine learning to system modeling problems
  • Automation and AI-support of MBSE activities
  • Data quality and privacy issues in AI for MBSE
  • MBSE approaches for explainable and fair AI
  • MBSE approaches for reliability analysis of AI components
  • MBSE approaches for robust AI
  • MBSE for AI
  • MBSE languages and tools for robustness evaluation processes of AI components
  • MBSE-based guidelines for AI trustworthiness
  • MBSE-based testing/analysis of AI components
  • Natural language processing applied to modelling, including Large Language Models (LLM) and Generative AI
  • Reinforcement learning for modelling tasks optimization
  • Trust and trustworthiness in AI ethics for MBSE


AI Requirements for an Effective Adoption in an MBSE Context

Jean-Michel Bruel
IRIT, University of Toulouse

Brief Bio
Jean-Michel Bruel has been head of the SM@RT team of the IRIT CNRS laboratory until September 2021. His research areas include the development of software-intensive Cyber-Physical Systems, and methods/model/language integration, with a focus on Requirements and Model-Based Systems Engineering. He defended his “Habilitation à Diriger des Recherches” in December 2006 and obtained in 2008 a full professor position at the University of Toulouse . He has been Laboratory Representative for the Toulouse 2 Jean Jaurès University from 2016 to 2020. He has been head of the Computer Science department of the Technical Institute of Blagnac in 2009-2012 and 2021-2023 and has been a member of the Strategic Research Committee of the IRIT CNRS laboratory since 2021. He is the holder of the Chair of Model-Driven Systems Engineering between AIRBUS and Toulouse 2 Jean Jaurès University since 2022 and joined the International Research Laboratory in AI in 2023.

Although recent advancements in artificial intelligence (AI) and large language models (LLMs) have shown promise, there remain inherent challenges that hinder their widespread adoption in critical systems development and operation. This talk will outline the essential criteria that AI approaches must meet to be effectively integrated into Model-Based Systems Engineering (MBSE).


AI-Enhanced Machine-Executable V-Model for MBSE

Stephan Rudolph
Institute for Aircraft Design, University of Stuttgart

Brief Bio
Stephan Rudolph holds the academic degrees of Dipl.-Ing., Dr.-Ing. and Priv.-Doz. in aerospace engineering from the faculty of Aerospace Engineering at the University of Stuttgart, Germany. Stephan is Head of the Design Theory and Similarity Mechanics Group and teaches several courses on digital engineering in the MSc specialization "Information Technology in Aerospace Engineering". In 2021 Stephan has been appointed adjunct associate professor at Swinburne University of Technology in Melbourne, Australia. Besides, Stephan is co-founder and associate of a small German engineering company Ingenieurgesellschaft für Intelligente Lösungen und Systeme mbH, a high-tech SME, specializing in digital design automation. Stephan’s research interests include formal methods in Model-Based System Engineering (MBSE) and formal engineering design synthesis methods using graph-based design languages, automatic model generation and design evaluation methods.

Applications of Artificial Intelligence (AI) to engineering design of complex cyber-physical systems (CPS) are getting currently more attention than ever despite the fact that the AI known today resembles more a collection of distinct methods than for some sort of closed theory of AI. The talk starts therefore with a scientific engineering needs analysis of a digital, machine-executable V-Model for MBSE and continues with an analysis of the different spaces which occur along the machine-execution of the V-Model. Then, based on this space analysis, the suitability of the different AI techniques is explored and illustrated using the current implementation of the Design Compiler 43 (IILS, 2017) as a further step towards a future “intelligent” design compiler. The key idea of a „design compiler“ as a machine which automates engineering design is around long since (Ward 1989, MIT) but long not believed to be feasible (Whitney, 1996, MIT). While in the electronic design automation (EDA) community so-called „silicon compilers“ are today the key element for the design and the production of new chips in a 3-month rhythm, the idea of digitazing and automating engineering design tasks in an intelligent design compiler still seems to be an open question for many.
The talk illustrates how an AI-enhanced machine executable V-Model for MBSE for the “intelligent” automation of complex engineering design tasks is achieved by means of graph-based design languages based on a UML-like notation. These graph-based design languages consist of ontologies (as UML class diagrams), rules (as UML model-to-model transformations or JAVA code) and production systems (as UML activity diagrams) and can be automatically compiled by means of the Design Compiler 43 into a design graph (UML instance diagram). This design graph represents a holistic and consistent abstract digital representation of the engineered product and is mapped via various design compiler plug-ins into various executable domain-specific language (DSL) models such as CAD-, MBS-, FEM-, and CFD-models. Several enhancements by AI methods in the V-Model are emphasized and illustrated with examples such as the design of satellite propulsion systems, the design of a FORMULA student racing car, automated packaging, piping and routing of subsystems during the system integration phase. (talk duration approx. 30-50 min, depending on details, tbd).
Ward, A.: “A Theory of Quantitative Inference Applied to a Mechanical Design Compiler”. PhD thesis. Massachusetts Institute of Technology, 1989.
Whitney, D.: “Why mechanical design cannot be like VLSI design”. Research in Engineering Design 8, 3 (1996), 125–138.
IILS Whitepaper “Total Engineering Automation with Graph-based Design Languages and the Design Compiler 43”, IILS mbH, Trochtelfingen, 2017.


Paper Submission: January 2, 2024 (expired)
Authors Notification: January 9, 2024 (expired)
Camera Ready and Registration: January 17, 2024 (expired)


Faouzi Adjed, Systemx, France
Rachid Benmokhtar, DSW, Valeo, France
Francis Bordeleau, Department of Software Engineering and Information Technology, École de Technologie Supérieure, Université du Québec, Canada
Alessio Bucaioni, Maelardalen University, Sweden
Paul T. Grogan, School of Computing and Augmented Intelligence, Arizona State University, United States
Hatem Hajri, SAFRAN Group, France
Moncef Hammadi, ISAE- SUPMECA, France
Sabrine Mallek, Supply chain and Information Systems Management, ICN Business School, CEREFIGE, Université de Lorraine, France
Goran Martinovic, J.J. Strossmayer University of Osijek, Croatia
Juliette Mattioli, Thales, France
Wassila Ouerdane, MICS CentraleSupelec, France
Hervé Panetto, CRAN, University of Lorraine, France
Olivia Penas, IRT SystemX, IRT SystemX, France
Bernhard Rumpe, Software Engineering - Department of Computer Science 3, RWTH Aachen University, Germany
Pierre-Alain Yvars, Quartz Lab, ISAE- SUPMECA, France

(list not yet complete)


Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.


After thorough reviewing by the workshop program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on digital support.
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library (
SCITEPRESS is a member of CrossRef ( and every paper is given a DOI (Digital Object Identifier).


MODELSWARD Workshops - MBSE-AI Integration 2024