White Paper: AI for Model-Based Design – Accelerating Engineering Innovation

Artificial Intelligence (AI) is rapidly becoming integral to engineered systems, from virtual sensors in vehicles to digital twins in industrial automation. However, building AI models is just one part of the challenge. The real test lies in deploying these models within complex, dynamic, and safety-critical environments. That’s where Model-Based Design (MBD) and tools like MATLAB and Simulink come in. 

This white paper introduces a practical, simulation-driven framework that enables engineers to integrate AI seamlessly throughout the product development lifecycle. By leveraging the full capabilities of MATLAB and Simulink, organizations can transition from raw data to embedded, certifiable AI components within a structured, traceable workflow. This document is essential reading for teams aiming to adopt AI responsibly and effectively in real-world systems.

What You’ll Learn from the White Paper

This White Paper outlines both the technical workflow and the real-world applications of combining AI with Model-Based Design.  

• Why AI Needs MBD – and Vice Versa
  Explore the mutual dependency between AI and Model-Based Design, why engineering-grade AI must be built within a rigorous simulation and verification environment, and how MBD provides the structure to make AI dependable and certifiable. 

•  AI Applications within MBD
  Learn how AI is applied throughout the stages of Model-Based Design: data preprocessing, system identification, predictive modeling, and control optimization.

• MBD-Driven AI Development Workflow
  Go through a complete, end-to-end workflow for developing AI in engineering systems. The workflow includes data collection and preprocessing in MATLAB, AI training and validation, system-level integration in Simulink, and final deployment using automated code generation.

• Use Cases Across Industries
  Read about field-tested applications across industries, including automotive (e.g., NOx virtual sensors, airflow estimation), industrial automation (AI-powered digital twins), energy (transformer health monitoring), and aerospace (reinforcement learning for UAV control).

• Advantages of Using MATLAB & Simulink
  Discover the technical and strategic benefits of MATLAB and Simulink: a unified toolchain, hybrid modeling, certifiable code generation, hardware support, and compliance with standards such as ISO 26262 and DO-178C.

• Future Outlook
  Explore key trends shaping the future of AI in engineering, such as real-time closed-loop control, synthetic data generation, explainable AI, and the democratization of AI tools for domain engineers.

Why This Matters  

Engineering teams today are under pressure to deliver smarter, faster, and safer systems. The combination of AI and Model-Based Design is a game-changer; it transforms AI from an experimental capability into a production-ready solution that integrates seamlessly with trusted system development frameworks.

Whether you’re building the next-gen vehicle powertrain, designing predictive maintenance for industrial assets, or simulating resilient flight control systems, this white paper provides a clear roadmap for making AI a trusted part of your engineering toolkit.

Featured products

All products mentioned are developed by MathWorks®:

• MATLAB
• Simulink

Learn more

• Blog: MATLAB and Simulink capabilities for AI with Model-Based Design
  Learn to integrate AI techniques with Model-Based Design (MBD) using MATLAB and Simulink. The blog highlights how virtual sensor modeling, reduced-order modeling, and reinforcement learning optimize system performance, accelerate development, and improve real-time applications.
  Read the blog
• On-demand webinar: AI for Model-Based Design: Reduced Order Modeling
  Learn to distill complex systems into simplified models; ROM streamlines the design process, making simulations faster, more efficient, and easier to manage.
  Watch the on-demand webinar
• On-demand webinar: Reinforcement Learning Workflow in MATLAB
  Learn how to apply Reinforcement Learning using MATLAB® and Simulink® products
  Watch the on-demand webinar
• On-demand webinar: AI for Model-based Design: Virtual Sensor Modeling
  Learn to develop virtual sensor models using advanced AI techniques such as feedforward neural networks, LSTMs, and decision trees.
  Watch the on-demand webinar