MBSE + AI Associate (C‑MBSE+AI‑A)

1. Training Introduction

The MBSE + AI Associate (C‑MBSE+AI‑A) program is designed to provide foundational and intermediate-level knowledge in Model-Based Systems Engineering (MBSE) integrated with Artificial Intelligence (AI) applications. This training empowers participants to understand, model, and analyze complex systems while leveraging AI for predictive insights and performance optimization.

The course combines theory, hands-on modeling exercises, and practical AI applications, preparing participants to support MBSE initiatives, system simulations, and decision-making in engineering, technology, and infrastructure domains.

 

2. Training Objective

The program aims to enable participants to:

• Understand core principles of MBSE and AI in systems engineering.
• Develop system models using MBSE techniques and tools.
• Apply AI for system analysis, predictive modeling, and optimization.
• Support system design, validation, and performance evaluation.
• Build competence to contribute to MBSE + AI projects in professional environments.

 

3. Targeted Group

• Early-career systems engineers and technical professionals
• AI practitioners seeking applications in systems engineering
• Project managers and analysts supporting MBSE projects
• Professionals in aerospace, defense, automotive, energy, and smart infrastructure
• Technical team members seeking certification in MBSE and AI integration

 

4. Course Duration

12–16 Days

• Standard comprehensive programme: 16 days
• Condensed programme for professionals with prior MBSE knowledge: 12 days

 

5. Training Methodology

• Instructor-led sessions with interactive discussions
• Case studies and real-world MBSE + AI applications
• Hands-on exercises with MBSE tools (e.g., SysML, Cameo Systems Modeler)
• AI-based system simulation and analysis exercises
• Group workshops for system design, validation, and scenario modeling
• Capstone project integrating MBSE principles and AI for a selected system
• Assessment through practical exercises, model deliverables, and final presentation

 

6. Course Content

Module 1: Introduction to MBSE and AI

• Fundamentals of MBSE and AI integration
• Benefits and applications in systems engineering
• Core principles, terminologies, and standards

Module 2: Systems Thinking and Complexity

• Systems thinking concepts
• Understanding interdependencies and feedback loops
• Complexity in engineering systems

Module 3: MBSE Modeling Frameworks

• SysML and UML for system modeling
• MBSE standards (OMG, INCOSE)
• Model creation, documentation, and traceability

Module 4: Requirements Engineering

• Capturing and managing system requirements
• Traceability and validation of requirements
• AI-assisted requirements analysis

Module 5: System Architecture Modeling

• Functional, logical, and physical architectures
• Interface definitions and allocation
• AI tools for architecture optimization

Module 6: Model Validation and Simulation

• Verification and validation techniques
• Simulation of system behaviors
• AI-assisted predictive simulation

Module 7: Data-Driven Systems Analysis

• System data collection and preprocessing
• Performance metrics and KPIs
• AI for predictive analysis and anomaly detection

Module 8: AI Applications in MBSE

• Machine learning, predictive analytics, and decision support
• Scenario modeling and forecasting
• Integrating AI into MBSE workflows

Module 9: Risk Assessment and Reliability

• Identifying risks and potential failures
• Reliability and maintainability considerations
• AI-assisted risk prediction

Module 10: Systems Integration

• Planning integration of subsystems
• Verification and validation processes
• AI applications in testing and fault detection

Module 11: Optimization Techniques

• Process and system optimization
• Multi-objective optimization using AI
• Performance enhancement strategies

Module 12: MBSE for Cyber-Physical Systems

• Applications in IoT, autonomous systems, and smart infrastructure
• Cybersecurity and resilience in system modeling
• AI-driven monitoring and predictive maintenance

Module 13: Project Management in MBSE + AI

• Agile and traditional MBSE project management
• Resource, schedule, and risk management
• AI tools for project monitoring

Module 14: Digital Twin and Virtual Modeling

• Concepts of digital twins
• Virtual modeling for testing and optimization
• AI-driven simulations for system monitoring

Module 15: Capstone Project – MBSE + AI Implementation

• Model a selected system using MBSE principles
• Apply AI for simulation, analysis, and optimization
• Present recommendations and findings

Module 16: Future Trends in MBSE + AI

• Emerging AI and MBSE techniques
• Applications in Industry 4.0 and smart infrastructure
• Preparing for next-generation MBSE + AI challenges

 

7. Expected Learning Outcomes

Participants will be able to:

• Understand and apply MBSE principles in real-world systems.
• Use AI techniques for predictive modeling, system simulation, and optimization.
• Develop system architectures and models following MBSE standards.
• Conduct performance analysis, risk assessment, and scenario evaluation.
• Contribute to MBSE + AI projects across engineering and technology sectors.
• Achieve professional recognition as MBSE + AI Associate (C‑MBSE+AI‑A).

 

8. Certificate of Completion

Upon successful completion of all modules, practical exercises, and the capstone project, participants will receive:

Certificate of Completion

MBSE + AI Associate (C‑MBSE+AI‑A)

Issued by FOTADE Training, Research and Resource Development Centre

This certificate validates the participant’s competency in MBSE principles, AI integration in systems engineering, and readiness to contribute effectively to professional projects.