DGTLENG Course Catalog

The definition course. What digital engineering is, how it works, what it is not, how it evolved, and where it's headed — grounded in a working definition built for the AI era.

Understanding the two foundational concepts that connect models to reality — the digital thread for traceability and the digital twin for live system representation.

The mental models that underpin digital engineering — systems, boundaries, interfaces, emergence, feedback, and complexity. How to think about engineered systems before modeling or simulating them.

Requirements as data, not documents. INCOSE standards, traceability, MBSE for requirements, and how AI is transforming requirements analysis.

Where 'information streams' become concrete infrastructure — the digital thread, PLM, configuration management, and interoperability standards that connect engineering data across the lifecycle.

A practitioner survey of the computational methods that underpin engineering — FEA, CFD, optimization, uncertainty quantification — and how AI has changed and augmented each. What every DE practitioner needs to know.

MBSE as a methodology within digital engineering — what it is, the landscape of languages and approaches (SysML v1, SysML v2, Arcadia, AADL, OPM), core modeling elements, and how MBSE connects to the broader DE ecosystem.

From architecture to fleet scale — designing, building, validating, and operating digital twins that connect system models to physical reality.

Systems thinking applied to real architecture decisions — trade-offs, interface design, architecture evaluation, and patterns across engineering domains.

Using structured data from models, simulations, and operations to make better engineering decisions — trade studies, risk analysis, and decision frameworks.

CI/CD for engineering — automating the digital thread with model checks, simulation pipelines, automated V&V, and intelligent orchestration.

Practical machine learning for engineering problems — surrogates, anomaly detection, NLP for technical documents, and deploying AI in real engineering workflows.

Advanced synthesis of computational methods and AI applied to engineering design at scale — generative design, surrogate-based optimization, reinforcement learning, multi-objective trade-offs, and integrating AI design tools into production workflows.

AI agents that perform engineering work — not just automation but systems that take initiative, make decisions within constraints, and collaborate with humans. Design agents, verification and validation agents, human-AI teaming, trust, and governance.

Organizational transformation for digital engineering — multi-program adoption strategies, governance frameworks, culture change, workforce development, and measuring ROI. The hardest problems in DE are not technical.

Designing, verifying, and certifying products that sense, decide, and act — autonomous vehicles, adaptive systems, self-healing infrastructure. Safety and certification as the central engineering challenges.

Cross-discipline integration, enterprise digital threads, supply chain data exchange, and governing the thread across organizations — when the digital thread spans disciplines, organizations, and supply chains.

Emerging technologies and their impact on digital engineering — foundation models, self-evolving digital twins, quantum computing for simulation, immersive engineering environments, and the evolving role of the engineer.