AI-ML for Systems Engineering

1. Training Introduction

The AI-ML for Systems Engineering programme is designed to equip modern systems engineers with the ability to integrate Artificial Intelligence (AI) and Machine Learning (ML) into the design, modelling, verification, operation, and management of complex systems.

In today’s engineering environment—characterized by cyber-physical systems, autonomous platforms, large-scale infrastructure, aerospace systems, multimodal transport, defence, energy, utilities, manufacturing, and digital ecosystems—AI provides powerful capabilities for prediction, optimization, automation, and intelligent decision-making.

This course bridges the traditional systems engineering (SE) discipline with AI-ML techniques to enhance system lifecycle performance, improve reliability, reduce risk, and strengthen systems-of-systems (SoS) analysis.

 

2. Training Objective

The programme aims to:

• Provide systems engineers with a practical understanding of AI-ML technologies.
• Integrate AI-ML methods into systems engineering processes and life-cycle management.
• Strengthen capabilities in modelling, simulation, verification, validation, and digital twins.
• Equip participants to design intelligent, adaptive, safe, and optimized systems.
• Develop hands-on skills in modern AI tools, algorithms, and engineering applications.
• Provide a structured framework for AI-enabled Systems Engineering (AI-SE) practice.

 

3. Targeted Group

This training is ideal for:

• Systems engineers and systems architects
• Engineering managers and project engineers
• Aerospace, defence, automotive, telecom, energy, and infrastructure engineers
• Control systems engineers and reliability engineers
• AI/ML practitioners working in engineering environments
• Engineering consultants, analysts, and technology planners
• Graduate engineers and technical officers

 

4. Course Duration

16 Days (Comprehensive Programme)
12 Days (Accelerated Professional Version)

 

5. Training Methodology

• Instructor-led technical lectures
• Hands-on AI-ML modelling and simulation labs
• Case studies from aerospace, energy, utilities, manufacturing, and defence
• Group exercises aligned with systems engineering lifecycle
• Practical applications using Python, MATLAB, Simulink, SysML, and digital twin tools
• Peer-reviewed design and modelling assignments
• Capstone project: AI-enabled Systems Engineering solution

 

6. Course Content

Module 1: Introduction to AI-ML and Systems Engineering (SE)

• Overview of AI, ML, and data-driven engineering
• Role of AI in systems engineering discipline
• AI-SE frameworks and standards

 

Module 2: Systems Thinking and Complex Systems

• Systems-of-systems (SoS)
• Complexity, emergence, feedback loops
• AI for complexity analysis

 

Module 3: System Modelling and SysML Foundations

• SysML diagrams for AI-related systems
• Requirement modelling
• Linking SysML with AI-ML components

 

Module 4: Engineering Data for AI-ML

• System lifecycle data
• Data acquisition, cleansing, and validation
• Handling engineering, sensor, and operational datasets

 

Module 5: Machine Learning Fundamentals for Systems Engineers

• Supervised, unsupervised, and reinforcement learning
• ML model development workflow
• Feature engineering for engineering systems

 

Module 6: Simulation, Modelling & Digital Twin Concepts

• Digital twins for predictive and adaptive systems
• Integrating ML with simulation environments
• Real-time monitoring and simulation feedback loops

 

Module 7: Reliability Engineering with AI-ML

• Predictive maintenance models
• Failure Mode and Effects Analysis (AI-enhanced FMEA)
• Reliability prediction with ML

 

Module 8: AI for System Design & Optimization

• Design space exploration
• Multi-objective optimization using AI
• Trade-off analysis with ML models

 

Module 9: Control Systems and Reinforcement Learning

• RL applications in robotics, automation, and autonomous systems
• Intelligent control loops
• Adaptive and self-learning systems

 

Module 10: AI-Enabled Verification, Validation & Testing

• ML model testing frameworks
• Verifying systems with AI-based methods
• Testing autonomous and AI-embedded systems

 

Module 11: AI Safety, Ethics, and Risk Management

• AI risk classification
• Responsible AI principles
• Safety-critical systems (aerospace, defence, utilities)

 

Module 12: Cybersecurity for AI-enabled Systems

• Security threats to AI models and engineered systems
• Secure architecture for AI-SE
• Cyber-physical threat modelling

 

Module 13: Systems Architecture for AI-ML Integration

• Designing AI-ready architectures
• Data pipelines, cloud-edge integration
• AI modules & system interfaces

 

Module 14: AI for Lifecycle Systems Management

• AI for operations, logistics, maintenance, and sustainment
• ML for cost modelling and performance forecasting
• Lifecycle decision optimization

 

Module 15: Practical Labs – AI-ML Tools for Systems Engineers

Hands-on activities using:

• Python for ML model development
• Simulink integration with ML
• SysML system modelling
• Digital twin environment
• Engineering dataset analysis

 

Module 16: Capstone Project – AI-Enabled Systems Engineering Solution

Teams design an AI-enhanced subsystem or system addressing:

• Prediction
• Optimization
• Adaptive control
• Reliability improvement
• Lifecycle efficiency

Project deliverables include architecture design, model implementation, validation results, and deployment concept.

 

7. Expected Learning Outcomes

Participants will be able to:

• Apply AI-ML principles within the systems engineering lifecycle.
• Model complex engineering systems and embed AI-enhanced components.
• Use ML tools to support reliability, optimization, design, and performance analysis.
• Create digital twin systems capable of intelligent real-time decision-making.
• Evaluate AI safety, risk, and cybersecurity aspects.
• Architect and manage AI-enabled systems effectively.
• Demonstrate competence in modern AI-Driven Systems Engineering methodologies.

 

8. Certificate of Completion

Upon successful completion of all modules, assessments, and the final capstone project, participants will be awarded:

Certificate of Completion

AI-ML for Systems Engineering

Issued by FOTADE Training, Research and Resource Development Centre

The certificate verifies mastery in applying AI and Machine Learning to systems modelling, analysis, lifecycle engineering, reliability engineering, and intelligent system design in line with moder