Certified MBSE + AI Professional (C-MBSE+AI) Certification Program by Tonex
This certification equips engineers, system architects, and program managers with the skills to integrate AI into Model-Based Systems Engineering. It covers AI-driven automation, validation, trade-off analysis, digital twins, and lifecycle optimization within MBSE frameworks such as SysML, UAF, and NAFv4.
The program focuses on practical, hands-on application with case studies across aerospace, defense, automotive, medtech, and smart infrastructure systems.
Learning Objectives
By the end of the program, participants will be able to:
- Apply AI tools to automate requirements analysis and traceability.
- Integrate AI with SysML/UML models for system design and validation.
- Use AI-enhanced MBSE for trade space exploration and design optimization.
- Build and manage digital twins with predictive and adaptive capabilities.
- Assess risks, ethics, and governance in AI-augmented systems engineering.
- Lead MBSE transformation initiatives in their organizations with AI augmentation.
Target Audience
- Systems Engineers and Architects
- MBSE Practitioners and Tool Specialists
- Program/Project Managers in complex domains
- AI/ML Engineers moving into systems design
- Defense, Aerospace, Automotive, Energy, and Healthcare Engineers
Prerequisites
- Basic understanding of Systems Engineering
- Familiarity with MBSE methods (SysML preferred)
- Awareness of AI/ML fundamentals
(Optional prep courses: MBSE Fundamentals and AI/ML for Engineers)
Program Modules
Module 1: Foundations of MBSE and AI
- Evolution of MBSE (Vee Model → MBSE → Digital Engineering)
- Core AI/ML methods relevant to MBSE
- AI integration challenges in systems engineering
Module 2: AI-Augmented Requirements Engineering
- NLP for requirements extraction, analysis, and validation
- Detecting ambiguities and inconsistencies with AI
- Automated requirements-to-SysML traceability
Module 3: AI in System Modeling and Validation
- Embedding AI in SysML/UAF models
- AI-driven validation and verification of system models
- Case study: Spacecraft subsystem modeling with AI
Module 4: Trade Space Exploration with AI
- Multi-objective optimization using AI
- AI-guided decision-making for performance, cost, and risk
- Generative design within MBSE frameworks
Module 5: Digital Twins and AI
- Creating and managing AI-powered digital twins
- Predictive maintenance, anomaly detection, and lifecycle support
- Example: AI-enhanced digital twin of an aircraft system
Module 6: Risk, Governance, and Ethics in AI-MBSE
- Reliability and trustworthiness of AI-augmented MBSE models
- AI explainability and bias in engineering decisions
- Cybersecurity considerations for AI in MBSE environments
Module 7: Tools, Frameworks, and Case Studies
- AI + MBSE toolchain integration (Cameo, Rhapsody, Capella + AI engines)
- Use cases in aerospace, defense, automotive, medtech, and smart grids
- Hands-on workshop: AI-augmented MBSE project
Certification Exam
Domains and Weights:
- MBSE and AI Foundations – 15%
- AI in Requirements Engineering – 20%
- AI in System Modeling and Validation – 20%
- Trade Space Exploration – 15%
- Digital Twins + Lifecycle – 15%
- Governance, Risk, and Ethics – 10%
- Tools and Applications – 5%
Format:
- Passing Criteria: 70% overall and at least 60% in each domain