MathWorks Conference: Engineering the Future with AI-Enhanced Model-Driven Workflows

See how AI and Model-Based Design converge to deliver speed, quality, and safety in engineered systems. Learn how Model-Based Design is evolving to combine AI-driven innovation with proven development practices.

This keynote will provide a comprehensive look at BAE System's two-decade journey with MATLAB, Simulink, and associated toolsets. We’ll explore how these tools have been integral to advancing our engineering and development processes, touching on key milestones and innovations along the way. From leveraging MATLAB and Simulink for system design and simulation to our experiences with toolboxes and specialized tools like Polyspace, we’ll share insights into how these platforms have evolved and adapted to meet the demands of complex aerospace and defence projects. This presentation will offer both a historical perspective and a forward-looking view on how these technologies continue to shape our approach to engineering excellence.

As a Tier 1 automotive supplier handling a high volume of complex models for integration into ISO 26262–compliant products, Bosch must ensure efficient processes and methods across the entire development lifecycle. Johan will present how Bosch uses generative AI to create scripts and tooling to support efficient, repeatable development processes. The presentation will highlight how generative AI can support Model-Based Design workflows by leveraging Simulink APIs to perform tasks such as adapting and generating artefacts required for certification. 

Furthermore, Johan will touch on current and future work at Bosch, focused on moving towards the next step: integrating generative AI, via MCP-servers, into Simulink for tasks such as creating requirements, architectures, and models.

This presentation will provide an overview of how SYPAQ employs component-based environment modelling to provide a consistent environment framework across many disparate, highly complex Reinforcement Learning activities.

Engineering education must evolve to prepare graduates for real-world control and automation challenges. This talk presents an integrated approach at the University of Technology Sydney, where MATLAB and Simulink are used to bridge the gap between simulation and deployment.

Across a sequence of control and automation subjects, students progress from fundamental feedback control concepts to advanced optimal and AI-based control, and ultimately to industrial automation. Model-based Design is embedded throughout this process, supporting controller development, validation, and implementation.

Students deploy control algorithms to embedded systems using automatic code generation for rapid prototyping and later implement solutions on industrial platforms using PLCs within an Industry 4.0 environment. These activities integrate modern technologies such as the Industrial Internet of Things (IIoT), robotics, digital twins, virtual reality, computer vision, cloud computing, and cybersecurity.

This end-to-end workflow—from modelling and simulation to embedded systems and industrial automation—enables students to transition from theory to real-world systems, equipping them with practical skills aligned with modern engineering practice.

Learn how to take trained AI models—whether developed in MATLAB or imported from Python—and integrate them into Model‑Based Design workflows that lead to tested, hardware‑ready embedded systems. You will see a clear framework for testing AI behaviour in system context, optimising models for embedded constraints, and preparing designs for production deployment.

This session builds on the end‑to‑end embedded AI workflow by showing how to scale it across teams and projects using CI/CD, automated verification, and version control. You will gain practical guidance on establishing CI/CD workflows, improving confidence in AI behaviour, and enabling teams to move faster while maintaining quality.

Hear first-hand perspectives from today’s industry and academic speakers on how digital engineering and AI are being adopted in real-world engineering and research workflows. Panellists will discuss the tangible impact on productivity, decision making, and innovation, as well as the challenges of scaling, integration, and building trust in AI-enabled systems. Attendees will leave with practical lessons and forward-looking ideas they can apply to accelerate and de-risk digital engineering initiatives in their own organisations.

Join us for a relaxed networking hour to connect with peers, partners, and MathWorks experts. Enjoy complimentary drinks and canapés as you mingle, share ideas, and celebrate two decades of innovation together.