Building End-to-End AI/ML Workflow Blueprints for Safety- and Mission-Critical Systems Featuring Ansys, part of Synopsys

AI/ML is foreseen to be integrated into safety- and mission-critical aerospace and defense systems. Engineering teams now face a hard question: how do you develop, deploy, and verify AI-enabled software with the predictability, traceability, and evidence that certification requires? Lynx and Ansys, part of Synopsys have collaborated to showcase a continuous, closed-loop engineering workflow that links system requirements, AI/ML development, embedded deployment, and system-level verification in an example blueprint built around a Helicopter Emergency Stopping (HES) use case.

On the Ansys side, the demonstration follows a Digital Engineering approach and is based upon a variety of tools to join MBSE (Model Based Systems Engineering) and Simulation: Software Architect Modeler (SAM), medini analyze, SCADE Suite, AVxcelerate Autonomy, AVxcelerate Sensors and Systems Tool Kit. Lynx provides the deterministic embedded execution environment with MOSA.ic.AI, a platform for running AI workloads on predictable CPU/GPU runtime environments designed for mission-critical operation. MOSA.ic.AI provides secure partitioning for mixed-criticality architectures and transforms AI/ML models trained anywhere, to execute bounded and deterministically in this environment. Together, these solutions support system architecture modeling, safety analysis, scenario-based simulation, AI/ML design, development and deployment, model-based software engineering and code generation, and requirements verification. The engineering workflow enables rapid prototyping, ML-model re-training and turnaround, moving from software-in-the-loop simulation to target-based and hardware-in-the-loop verification.

Attendees will learn how to:

• Use simulation to discover AI-related system underperformance early, before implementation reach target hardware.
  
• Maintain end-to-end traceability from requirements through AI/ML lifecycle artifacts to verified behavior.
  
• Integrate deterministic control software and AI/ML functions in a unified, modular, mixed-criticality embedded architecture.
  
• Transition from SiL to HiL testing in a predictable embedded runtime environment.
  
• Create and capture safety evidence throughout the workflow to support assurance and certification.

This session is part of the The LYNX MOSA.ic.AI Webinar Series. Registration/event page here.