Certified Model-Based Systems Engineering + AI Professional (C‑MBSE+AI‑P)

1. Training Introduction

The Certified Model-Based Systems Engineering + AI Professional (C‑MBSE+AI‑P) program is designed to equip engineering and technology professionals with advanced skills in Model-Based Systems Engineering (MBSE) integrated with Artificial Intelligence (AI) applications. The course focuses on designing, analyzing, and managing complex systems using MBSE principles while leveraging AI for enhanced system optimization, predictive analytics, and decision-making.

Participants will gain hands-on experience in system modeling, AI-driven simulations, and data-driven system analysis for industries such as aerospace, defense, energy, automotive, and smart infrastructure.

 

2. Training Objective

The program aims to enable participants to:

• Master MBSE principles for system architecture, modeling, and lifecycle management.
• Integrate AI techniques with MBSE for predictive modeling and optimization.
• Develop, validate, and simulate complex system models.
• Apply data-driven analytics for system performance enhancement.
• Manage multidisciplinary system projects with MBSE and AI tools.
• Achieve professional certification recognition in MBSE and AI systems engineering.

 

3. Targeted Group

• Systems engineers and MBSE practitioners
• AI specialists and data scientists in engineering domains
• Project and program managers handling complex engineering systems
• Aerospace, automotive, energy, and defense engineers
• Technical leads in smart infrastructure, IoT, and cyber-physical systems
• Professionals seeking certification in MBSE and AI applications in systems engineering

 

4. Course Duration

12–16 Days

• Standard comprehensive programme: 16 days
• Condensed programme for experienced professionals: 12 days

 

5. Training Methodology

• Instructor-led lectures and interactive discussions
• Case studies on complex systems across industries
• Hands-on exercises with MBSE modeling tools (e.g., Cameo Systems Modeler, SysML)
• AI-driven simulations for system optimization and predictive analysis
• Group workshops for architecture design, system validation, and scenario modeling
• Capstone project integrating MBSE and AI for a real-world system
• Assessment through exercises, project deliverables, and final presentations

 

6. Course Content

Module 1: Introduction to MBSE and AI in Systems Engineering

• Fundamentals of MBSE and AI integration
• Benefits and applications in complex systems
• Key principles, standards, and frameworks

Module 2: Systems Thinking and Lifecycle Management

• System thinking principles
• System lifecycle phases: concept, design, implementation, operation, decommissioning
• Managing system complexity

Module 3: MBSE Frameworks and Standards

• SysML and UML for system modeling
• OMG MBSE standards
• Model-based documentation and traceability

Module 4: Requirements Engineering in MBSE

• Capturing and managing system requirements
• Requirements traceability and validation
• AI-assisted requirements analysis

Module 5: System Architecture and Design

• Functional, logical, and physical architecture modeling
• Interface definition and allocation
• AI applications in system design optimization

Module 6: Model Simulation and Validation

• Model verification and validation techniques
• Simulation tools and methodologies
• AI-driven predictive simulation

Module 7: Data-Driven Systems Analysis

• Collecting and preprocessing system data
• Using AI for anomaly detection and performance prediction
• Key performance indicators (KPIs) and metrics

Module 8: AI in System Decision-Making

• Machine learning, deep learning, and reinforcement learning in MBSE
• Predictive analytics for design and operational optimization
• Scenario analysis and decision support

Module 9: Risk Assessment and Reliability Engineering

• Risk modeling and mitigation strategies
• Reliability, availability, maintainability, and safety (RAMS)
• AI-assisted risk prediction and management

Module 10: Systems Integration and Verification

• Integration planning for complex systems
• Verification and validation processes
• AI applications in testing and fault diagnosis

Module 11: Optimization and Performance Enhancement

• Multi-objective optimization techniques
• AI for resource allocation and efficiency improvement
• Simulation-based optimization

Module 12: Model-Based Systems Engineering for Cyber-Physical Systems

• MBSE in IoT, smart grids, and autonomous systems
• AI integration in cyber-physical system design
• Security and resilience considerations

Module 13: Project Management in MBSE + AI

• Agile and traditional systems engineering project management
• Resource, schedule, and risk management
• AI-enabled project monitoring and forecasting

Module 14: Digital Twin and Virtual System Modeling

• Concept of digital twins
• Building virtual system models for testing and optimization
• AI-driven simulations for real-time monitoring

Module 15: Capstone Project – MBSE + AI Implementation

• Design, model, simulate, and optimize a real-world system
• Apply MBSE principles and AI techniques for predictive and operational enhancement
• Present findings, recommendations, and implementation plan

Module 16: Future Trends in MBSE and AI

• Emerging AI and ML techniques for systems engineering
• Industry 4.0, smart infrastructure, and autonomous systems
• Preparing for next-generation MBSE challenges

 

7. Expected Learning Outcomes

Participants will be able to:

• Develop, simulate, and validate complex system models using MBSE principles.
• Integrate AI techniques for predictive analytics, optimization, and decision support.
• Analyze, monitor, and improve system performance using data-driven approaches.
• Apply MBSE + AI in diverse sectors, including aerospace, energy, automotive, and smart infrastructure.
• Lead multidisciplinary projects with advanced MBSE and AI methodologies.
• Achieve professional recognition as a Certified Model-Based Systems Engineering + AI Professional (C‑MBSE+AI‑P).

 

8. Certificate of Completion

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

Certificate of Completion

Certified Model-Based Systems Engineering + AI Professional (C‑MBSE+AI‑P)

Issued by FOTADE Training, Research and Resource Development Centre

This certificate validates the participant’s expertise in MBSE, AI-driven system optimization, and professional competency in advanced systems engineering practices.