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Mathworks
Future-Ready Defense Engineering: From Digital Threads to Mission-Level Prediction
Abstract.
As defense systems become increasingly autonomous, software-defined, and multidisciplinary, engineering organizations must move beyond static architectures and descriptive models toward predictive, simulation-driven digital engineering. Future-ready Defense engineering requires executable system models that connect requirements, architecture, design, verification, and operations, enabling engineers to evaluate mission outcomes, system performance, and trade-offs before deployment. This session demonstrates how simulation-driven digital threads support more agile development, faster decision-making, and reduced integration risk across the defense lifecycle. Two Defense-focused autonomous systems illustrate this approach. An Autonomous Underwater Vehicle (AUV) example demonstrates how mission planning, vehicle dynamics, sensing, control, propulsion, energy management, autonomy, and environmental interactions can be evaluated together within a unified system simulation. Engineers can assess mission success, identify failure modes, visualize complex behaviors, and explore design alternatives early in development. A second example, based on an Autonomous Ground Vehicle (AGV) validation framework, shows how requirements, architecture models, environmental conditions, hardware and software variants, and executable tests can be integrated into a cohesive digital thread. This enables rapid trade-space exploration, scalable verification across thousands of scenarios, and continuous evaluation of mission effectiveness while maintaining end-to-end traceability. Together, these examples highlight how system-level simulation, lifecycle model reuse, integrated verification, automation, and standards-based interoperability help defense organizations develop and validate increasingly complex autonomous systems with greater confidence, speed, and mission readiness.
Agentic Engineering Workflows for Systems Engineering and FPGA/SoC development
Abstract.
Agentic AI is transforming Model-Based Systems Engineering (MBSE) by enabling intelligent automation of complex engineering workflows while maintaining the rigor, traceability, and governance required for modern system development. By combining AI agents with MathWorks System Composer, engineers can accelerate the creation, analysis, and refinement of system architectures while maintaining full engineering oversight.
Key capabilities include:
• Automated requirements analysis and decomposition
• Generation and refinement of system architectures
• Traceability management between requirements, architecture, and implementation
• Model consistency checking and validation
• Orchestration of multi-step engineering workflows spanning system, software, FPGA, and SoC development
Agentic workflows combine large language models with engineering tools, planning capabilities, memory, and feedback loops to automate repetitive tasks and support design exploration. Rather than replacing engineers, these workflows augment engineering teams by reducing manual effort and allowing them to focus on higher-value design decisions.
This presentation demonstrates how System Composer provides the structured architectural foundation needed to guide AI-driven workflows and produce transparent, reviewable engineering artifacts. It further shows how architectural intent can be connected to downstream software, FPGA, and SoC implementation workflows, enabling an end-to-end digital thread from requirements through deployment.
Attendees will learn by means of practical use-cases how Agentic AI can help to:
• Scale digital engineering practices across multidisciplinary teams
• Improve productivity through intelligent workflow automation
• Accelerate development from requirements and architecture to software, FPGA, and SoC implementation and verification
• Enhance traceability and consistency across the entire system lifecycle