Model-Based Systems Engineering and Systems Modeling Language
The course is dedicated to the methods of Model‑Based Systems Engineering (MBSE) — an approach in which the model becomes the central engineering artefact. Students will learn to use system modelling as a key tool for designing complex technical and digital systems.
Model‑Based Systems Engineering (MBSE) enables the transition from a textual description of a system to a formal model that integrates requirements, architecture and component interactions. In essence, MBSE addresses the key question: “How can a system be formally described as an engineering model?”
Upon completing the course, students will have acquired comprehensive knowledge of the fundamental principles of systems engineering, MBSE methods and the structure of a system model. They will be proficient in the Systems Modelling Language (SysML) and understand the techniques for modelling requirements, architecture, component interactions and system behaviour. Students will also grasp the principles of requirement traceability and be aware of the capabilities and limitations of Large Language Models (LLMs) in the context of system modelling.
In terms of practical abilities, graduates of the course will be able to construct system models for complex systems, model system requirements and develop architectural system models. They will know how to describe component interactions, simulate system operation scenarios and analyse the consistency of system models. Additionally, they will be skilled at identifying architectural contradictions and leveraging LLMs for system model analysis.
Furthermore, students will gain hands‑on skills in applying system modelling techniques and using architectural system analysis tools. They will learn to create SysML diagrams, apply requirement traceability methods, prepare system documentation and critically evaluate the outputs of LLM assistants.
The course is built around several key concepts. First, MBSE is presented as an approach where the model integrates requirements, architecture, functions, component interactions and system behaviour — ultimately enhancing the consistency of complex system design. Second, the system model is viewed as an integration of different perspectives (a requirements model, structural architecture, an interaction model and a behaviour model) that together form a holistic view of the system.
Third, the Systems Modelling Language (SysML) is introduced as the primary language for describing complex systems. Students will study several key model types, including Requirements diagrams, Block Definition Diagrams (BDD), Internal Block Diagrams, Activity Diagrams and Sequence Diagrams. Fourth, the course highlights the connection between system modelling and digital twins: a formal description of the system’s structure and behaviour makes it possible to simulate system operation, analyse various operational scenarios and predict system behaviour.
The practical component of the course centres on a cross‑cutting system modelling project, during which students develop a model of a chosen digital system. Large Language Models (LLMs) play an active role in the learning process, serving as versatile tools in three main capacities. As a handbook, an LLM can explain systems engineering and modelling language terminology. As an analytical assistant, it helps with domain analysis, identifying potential architectural solutions and generating system model variants. As an engineering opponent, an LLM assists in detecting requirement contradictions, architectural inconsistencies and incomplete system scenarios.
A mandatory condition throughout is the engineering verification of any recommendations provided by LLMs, ensuring that students develop both technical proficiency and critical thinking when working with AI tools.
OBJECTIVES
Mastering MBSE principles;
Learning system modelling languages;
Mastering SysML;
Developing architectural system description skills;
Modelling system behaviour;
Integrating different system views;
Fostering systemic engineering thinking;
Building a culture of critical LLM assistant use in system modelling.
KEY TASKS
Learning MBSE principles;
Mastering system model building methods;
Learning SysML;
Mastering requirement modelling methods;
Learning structural system modelling;
Mastering component interaction modelling;
Learning system behaviour modelling;
Mastering system model integration;
Learning requirement traceability principles;
Mastering system model verification;
Developing digital system modelling skills;
Using LLM as an engineering analysis tool.
Main topics of the course:
1. Complexity of modern digital systems and the need for modelling. Explores the growing complexity of digital and cyber‑physical systems and explains how modelling helps manage this complexity, replacing limited document‑based design.
2. Model‑Based Systems Engineering (MBSE). Introduces MBSE as an approach where the model becomes the central engineering artefact, enabling a shift from document‑based to unified system modelling.
3. System model as an engineering artefact. Covers the key elements of a system model (needs, requirements, functions, architecture, behaviour) and its role in supporting engineering decisions.
4. Modelling languages: UML and SysML. Provides an overview of key modelling languages, highlighting the differences between UML and SysML and their specific applications in systems engineering.
5. Requirements Engineering in MBSE. Focuses on transforming user needs into system requirements and emphasising the importance of requirement traceability in architecture design.
6. Structural system modelling. Examines system architecture, components, interfaces, and modularity, showing how architectural levels contribute to effective system design.
7. Component interaction within a system. Addresses data flows and interfaces between subsystems, helping to visualise how different parts of a system communicate and work together.
8. Functional system architecture. Discusses functional decomposition methods and demonstrates how system functions are linked to the overall architecture.
9. System interaction scenarios. Explores operational scenarios of system functioning, illustrating how components interact in real‑world use cases.
10. System states and lifecycle. Covers system states and the events that trigger transitions between them, providing a clear view of the system’s lifecycle.
11. Integrating the system model. Shows how to align requirements, architecture, and system behaviour into a unified model, ensuring consistency across all design aspects.
12. Verifying and validating system models. Introduces methods for checking model correctness and identifying inconsistencies, ensuring the model meets engineering standards.
13. MBSE and digital system architecture. Highlights specific features of digital platform and distributed service architecture, demonstrating how MBSE principles apply in these contexts.
14. MBSE and digital twins. Explains the connection between system models and digital twins, showing how formal system descriptions support the creation of digital replicas.
15. Working on the final system modelling project. Offers hands‑on experience in developing a complete system model, including identifying and fixing inconsistencies with the support of LLM tools.
16. Consultations on final projects. Provides targeted support for refining project work, helping students audit and polish their models before the final defence.
