@article{59513,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>The increasing complexity of modern technical systems necessitates innovative approaches such as Model-Based Systems Engineering (MBSE). In this context, using Artificial Intelligence (AI) emerges as a key enabler for practical application and efficiency improvement. This article introduces a maturity model for AI-based assistance systems in MBSE. It helps companies assess their current automation level in MBSE activities, providing a foundation for strategic planning of process improvements.</jats:p>}},
  author       = {{Bernijazov, Ruslan and Dumitrescu, Roman and Hanke, Fabian and von Heißen, Oliver and Kaiser, Lydia and Tissen, Denis}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  number       = {{s1}},
  pages        = {{96--100}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{AI-Augmented Model-Based Systems Engineering}}},
  doi          = {{10.1515/zwf-2024-0123}},
  volume       = {{120}},
  year         = {{2025}},
}

@inproceedings{60160,
  author       = {{Förster, Felix and Bausen, Steffen and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman and Bursac, Nikola}},
  booktitle    = {{Stuttgarter Symposium für Produktentwicklung SSP 2025}},
  editor       = {{Hölzle, Katharina and Kreimeyer, Matthias and Roth, Daniel and Maier, Thomas and Riedel, Oliver}},
  location     = {{Stuttgart}},
  pages        = {{102--113}},
  publisher    = {{Fraunhofer IAO, Stuttgart}},
  title        = {{{Chances of smart Views: Integration of Stakeholder perspectives using videobased Views in MBSE}}},
  doi          = {{10.18419/opus-16366}},
  year         = {{2025}},
}

@inproceedings{58763,
  abstract     = {{Utilizing data is crucial for economic success, but a lack of interoperability and concerns about the misuse of ones own data are hindering the cross-organizational use of data. Dataspaces provide the infrastructure necessary to integrate heterogeneous data sources within an organization or ecosystem, enabling seamless data interaction and interoperability. In addition, data spaces strengthen data sovereignty through their decentralized nature, which enables organizations to effectively control and manage their data. However, challenges persist in managing the complexity and dynamic nature of dataspaces, requiring significant resources and technical expertise. The decentralized nature leads to a large and diverse number of stakeholders, who need to agree on the use and scope of a dataspace. Modeling is a common approach to cope with technical complexity and heterogeneous stakeholders. In this paper, we propose a version of SysML and a corresponding method that focus on the modelling of data spaces. We provide a dataspace modelling method to unify the understanding of dataspaces and scope among all stakeholders to simplify the design and development process.}},
  author       = {{Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  keywords     = {{Dataspaces, Modelling, SysML, Gaia-X, System Specification}},
  location     = {{Sharjah, United Arab Emirates }},
  publisher    = {{IEEE}},
  title        = {{{Using SysML as a Modelling Language for Dataspaces}}},
  doi          = {{10.1109/ictmod63116.2024.10878227}},
  year         = {{2025}},
}

@inproceedings{56166,
  abstract     = {{Developing Intelligent Technical Systems (ITS) involves a complex process encompassing planning, analysis, design, production, and maintenance. Model-Based Systems Engineering (MBSE) is a key methodology for systematic systems engineering. Designing models for ITS requires harmonious interaction of various elements, posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we generated a dataset for modeling, using prompt engineering on large language models. The generated artifacts can aid engineers in MBSE design or serve as synthetic training data for AI assistants.}},
  author       = {{Kulkarni, Pranav Jayant and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{DS 130: Proceedings of NordDesign 2024}},
  editor       = {{Malmqvist, J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}},
  keywords     = {{Data Driven Design, Design Automation, Systems Engineering (SE), Artificial Intelligence (AI)}},
  location     = {{Reykjavik}},
  pages        = {{617--625}},
  title        = {{{Towards Automated Design: Automatically Generating Modeling Elements with Prompt Engineering and Generative Artificial Intelligence}}},
  doi          = {{10.35199/NORDDESIGN2024.66}},
  year         = {{2024}},
}

@inproceedings{56167,
  author       = {{Tissen, Denis and Wiederkehr, Ingrid and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{DS 130: Proceedings of NordDesign 2024, Reykjavik, Iceland, 12th - 14th August 2024}},
  editor       = {{ Malmqvist, J. and Candi, M. and Saemundsson, R. J.  and Bystrom, F. and Isaksson, O.}},
  location     = {{Reykjavik, Iceland}},
  title        = {{{Spearhead Data-Driven Model-Based Systems Engineering: Interview Study on Definition, Preconditions, Challenges, Potentials, and Use Cases}}},
  doi          = {{ 10.35199/NORDDESIGN2024.58}},
  year         = {{2024}},
}

@inproceedings{56169,
  author       = {{Tissen, Denis and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{DS 134: Proceedings of the 26th International DSM Conference (DSM 2024), Stuttgart, Germany}},
  editor       = {{Stowe, Harold and Langner, Christopher and Kreimeyer,  Matthias and Browning, Tyson R. and  Eppinger, Steven D. and Yassine, Ali A. }},
  location     = {{Stuttgart, Germany}},
  title        = {{{A Maturity Model for Data-Driven Model-Based Systems Engineering for Producing Companies}}},
  doi          = {{10.35199/dsm2024.13}},
  year         = {{2024}},
}

@inproceedings{58418,
  author       = {{Tissen, Denis and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 26th International DSM Conference (DSM 2024), Stuttgart, Germany}},
  publisher    = {{The Design Society}},
  title        = {{{A Maturity Model for Data-Driven Model-Based Systems Engineering for Producing Companies}}},
  doi          = {{10.35199/dsm2024.13}},
  year         = {{2024}},
}

@inproceedings{56168,
  author       = {{Tissen, Denis and Wiederkehr, Ingrid and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}},
  location     = {{Kyoto, japan}},
  title        = {{{Portals across domains: A data analysis task and documentation canvas for data-driven model-based systems engineering}}},
  year         = {{2024}},
}

@inproceedings{58479,
  author       = {{Kulkarni, Pranav Jayant and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of NordDesign 2024}},
  publisher    = {{The Design Society}},
  title        = {{{Towards Automated Design: Automatically Generating Modeling Elements with Prompt Engineering and Generative Artificial Intelligence}}},
  doi          = {{10.35199/norddesign2024.66}},
  year         = {{2024}},
}

@inbook{60161,
  author       = {{Tissen, Denis and Wiederkehr, Ingrid and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{System Innovation for an Artificial Intelligence Era }},
  editor       = {{Kin-Tak Lam, Artde Donald and Prior, Stephen D. and Shen, Siu-Tsen and Young, Sheng-Joue and Ji, Liang-Wen}},
  publisher    = {{CRC Press}},
  title        = {{{Portals across domains: A data analysis task and documentation canvas for data-driven model-based systems engineering}}},
  year         = {{2024}},
}