MATLAB EXPO 2026 United Kingdom

Agentic AI is transforming how engineers work—from writing code and building models to capturing requirements and validating system behavior. As AI accelerates creation at every stage, the need to produce evidence that the systems work as intended increases in proportion.

In this keynote, follow the engineering journey from initial idea through prototyping, production readiness, and system validation—and see how MATLAB^®, Simulink^®, and agentic AI accelerate every stage. Whether you're prototyping a new concept, refactoring an existing model, generating requirements, or validating system-level behavior, discover how AI agents paired with MATLAB and Simulink can help you move faster with results you can verify. See demonstrations spanning app building, system modeling and simulation, test harness creation, and scenario generation.

Hear how adopting safety-critical flight control computer architectures for a large UK programme triggered a step change in MATLAB^®, Simulink^®, and Model-Based Design capabilities at BAE Systems. Discover the operational drivers behind a Continuous at Sea Deterrent replacement, the cross-domain collaboration between aerospace and maritime engineers, and the architectural thinking that followed. Learn how large-scale modelling capability was rapidly established and the key challenges faced, including developing and evolving a large engineering team on such a complex programme. See how a close partnership with MathWorks helped in upskilling and enabling collaboration across BAE Systems, thereby accelerating adoption and maturity. Explore the impact achieved and what this experience means for the future of safety-critical system development at scale.

Modern software development has achieved rapid iteration cycles through the adoption of CI/CD pipelines, automation, and reusable components. However, digital twin and hardware model development often lag behind, creating a bottleneck for virtual validation and early integration.

In this presentation, learn about the concept of a model factory—a continuous, automated pipeline for hardware models inspired by proven software development practices. The model factory enables the systematic creation of validated hardware models, deployable artifacts, and reusable components, supporting scalable and repeatable virtual validation workflows.

You will also explore the key enablers required to realize a model factory in practice. This includes governance models to establish clear ownership and contribution rules, standardized tooling for CI-driven validation and packaging, and common interfaces to ensure interoperability across teams. Together, these elements enable measurable improvements in reuse, quality, and development speed, transforming hardware models into reliable, scalable engineering assets.

Engineers and scientists are tasked with designing increasingly complex systems and adopting modern practices, all while shortening development cycles. At the same time, generative and agentic AI are transforming how engineering work is performed. New capabilities in MATLAB^® and Simulink^® enhance proven engineering workflows while adding capabilities for AI-assisted approaches.

See new capabilities that accelerate exploration and development; help engineers manage larger and more complex data and models; support verification and validation activities; simplify deployment of algorithms, models, and applications; and provide new capabilities for aerospace missions and vehicles, wireless communications, and RF engineering. Together, these advances enhance day-to-day productivity and provide a foundation for effective AI-enabled workflows, helping engineers manage complexity, validate AI-generated results, and deliver with confidence.

Whether you’re exploring the latest AI technologies or looking to improve your existing engineering workflows, you’ll discover new ways to work more effectively with MATLAB and Simulink.

As engineering workflows increasingly span requirements, models, code, and test, traditional role boundaries are becoming blurred. This panel explores how requirements engineering, implementation, and verification are evolving from sequential hand‑offs into tightly coupled, continuously interacting activities. It examines which skills matter most for engineers operating in this environment, and how generative AI changes—not replaces—them, focusing on where GenAI delivers genuine value today: improving flow, reducing friction between disciplines, supporting decision‑making, and augmenting engineering judgement rather than automating it away.

Model-based systems engineering (MBSE) is often presented as a clear, top-down transformation. Vertical Aerospace’s experience has been anything but. In this talk, see a more realistic journey: a grassroots adoption of MATLAB^® and Simulink^® products for MBSE, built incrementally to deliver value in a fast‑moving eVTOL development environment.

Explore how a system model of the aircraft has evolved across functional, logical, and physical layers, each at different levels of maturity. While functional and logical modelling remain works in progress, the physical architecture has reached sufficient depth to support continuous integration workflows and downstream engineering use cases. In practice, the model has become a structured backbone for design data, enabling automated artefacts and integration with external tools, including wiring diagram generation.

An example of a downstream engineering use case has been cyberthreat assessment. See how a structured system model enables more efficient and scalable cyber assessments. By leveraging consistent architecture data, this approach reduces manual effort, improves traceability, and accelerates analysis workflows, illustrating how even a partially mature model can unlock significant downstream value.

The journey to this point has not been straightforward. Adoption has faced organisational resistance, limited formal training, and the ongoing challenge of balancing modelling effort with engineering delivery. Explore how the adoption of MBSE tools builds value over time, highlighting both the wins and the friction points.

Safety and cybersecurity are increasingly critical for modern, connected systems, yet they are often addressed through separate, document‑centric processes that remain disconnected from day‑to‑day model-based development. This separation leads to manual artifact creation, inconsistent data across disciplines, and high effort when designs change—despite growing expectations driven by standards such as ISO 26262, DO‑178/DO‑254, ISO/SAE 21434, DO‑326A, and IEC 62443.

This presentation shows how model-based systems engineering (MBSE) enables a unified digital thread for safety and security, leveraging MATLAB^® and Simulink^® to make assurance activities part of the engineering workflow. Using a warehouse robot as a practical end‑to‑end example, see how the same system architecture and behavioral models support both model-based safety analysis and model-based security analysis.

For safety, hazards are identified and traced directly to the architecture, faults are modeled with clear semantics, and impacts are assessed using static consistency checks and simulation-based validation. For security, architectural connectivity is used to identify assets, model threats, and estimate risk. Countermeasures and requirements are allocated back to the design and verified through simulation.

By unifying safety and cybersecurity within a single model-based environment, teams can shift assurance activities left, validate assumptions earlier, and perform systematic change impact analysis. When designs evolve, affected hazards, threats, and mitigations can be identified consistently, reducing manual effort while maintaining traceability and compliance. This session is intended for MATLAB and Simulink users looking to move from disconnected tools to a scalable, model-based approach where safety and cybersecurity are first‑class citizens of the digital thread.