Next-Generation Digital Engineering: AI, Knowledge Graphs, and Cognitive Digital Threads – Online Short Course 30 March 2027 - 8 April 2027 Online

Register Now

Next-Generation Digital Engineering: AI, Knowledge Graphs, and Cognitive Digital Threads – Online Short Course


  • From 30 March–8 April 2027 (2 Weeks, 4 Classes, 8 Total Hours)
  • Every Tuesday and Thursday at 1–3 p.m. Eastern Time (all sessions will be recorded and available for replay; course notes will be available for download)
  • In this new cutting-edge course, gain a full understanding of the basic principles, practices, and tools of Digital Engineering with an introduction to emerging cognitive applications of AI, Knowledge Graphs, and Cognitive Digital Threads to reach the full potential of digital transformation of products and processes to accelerate delivery and lower costs.
  • All students will receive an AIAA Certificate of Completion at the end of the course.

OVERVIEW
Over four classes, participants will not only gain an in-depth understanding of foundational Digital Engineering (DE) tools, principles, and practices but will also be introduced to the next evolution of DE – a Cognitive Digital Engineering approach. The purpose of a Digital Thread, as the foundation for digital transformation of systems engineering processes, is to synthesize and connect knowledge across heterogeneous functional domains and lifecycle epochs to accelerate materiel capability delivery and reduce total ownership costs. A concept is introduced for a Cognitive Digital Thread (CDT) which is a Digital Thread with enhanced semantic capability that allows formalized traceability relationships between development processes, system artifacts, and value-focused stakeholder decisions. A Cognitive Thread Network (CTN) is also introduced wherein each ingested artifact is mapped semantically across a distributed neural index, akin to how the brain creates associative pathways among neurons. The CTN enables implementation of a complete CDT – an unbroken chain of data traceability from raw sources through AI processing to a graph-based, ontologically aligned knowledge management capability. The CTN perpetually ingests, associates, reinforces, and evolves knowledge in a live, adaptive architecture that grows richer and more insightful with every experience. The CTN also minimizes the potential of hallucinations as well as reduces the time and cost of training a conventional AI Large Language Model leading to a more robust, cost-effective use of Machine Learning to accelerate delivery of a materiel capability.

LEARNING OBJECTIVES

  • Gain an in-depth understanding of Digital Engineering strategies, principles, practices, and tools.
  • Learn to think digitally, not digitized, and to focus on DE to transform lifecycle systems engineering processes to deliver knowledge enabling better decision making at the speed of relevance.
  • Be introduced to an advanced evolution of the traditional DE concept by incorporating cognitive capabilities to make integrable and interoperable use of all authoritative sources of truth.
  • Understand how Knowledge Graphs and Ontologies provide the foundation for development, training, and applications of AI Large Language Models to improve and accelerate system development processes and enable better authoritative decision making.
  • How to connect Mission Engineering, Systems Engineering, and Test & Evaluation to identify the knowledge of Who, What, Where, When, and How is required to enable authoritative decision making.

AUDIENCE
Anyone involved in the visioning, development, and delivery of materiel capabilities for either defense or commercial applications including scientists, engineers, project engineers, test engineers, program managers, and senior decision makers.

COURSE FEES (Sign-In To Register)
– AIAA Member Price: $595 USD
– AIAA Student Member Price: $395 USD
– Non-Member Price: $795 USD

CLASSROOM HOURS / CEUs: 8 classroom hours / 0.8 CEU/PDH

CANCELLATION POLICY: A refund less a $50.00 cancellation fee will be assessed for all cancellations made in writing prior to 5 days before the start of the event. After that time, no refunds will be provided.

CONTACT: Please contact Lisa Le or Customer Service if you have any questions about the course or group discounts.

Frequently Asked Questions

Outline
  • Class 1: Introduction to Digital Engineering
    • Digital Engineering History and Terminology
    • Digital Engineering Strategy
    • Digital Thread / Digital Twin Defined
    • Knowledge Graph and Ontologies – foundation of a Digital Thread
    • Descriptive, Predictive, and Prescriptive Models
    • Authoritative Digital Surrogates
    • Uncertainty Quantification and Mastering Risk
  • Class 2: Cognitive Digital Engineering
    • Cognitive Processes Defined
    • Semantic Web Technologies
    • Cognitive Thread Network
    • Digital Engineering Framework for Integrability and Interoperability
  • Class 3: Transforming Engineering Processes
    • Lifecycle Meta Model – Connecting Mission Engineering, Systems Engineering, and Test & Evaluation
    • Integrated Decision Support Key
    • Mission Engineering
    • Systems Engineering
      • Capability Based Knowledge Generation
      • Value-Focused, Risk-Informed Decision Analytics
    • Test and Evaluation
      • T&E Transformation Using Cognitive Digital Engineering
      • Model Based Test and Evaluation Master Plan
      • T&E as A Continuum
  • Class 4: Authoritative Decisioning and Applications of Cognitive Digital Engineering
    • Connecting a Cognitive Digital Thread and AI Large Language Models to Improve and Accelerate Capability Delivery
    • Accelerated Authoritative Decision Making – Observe, Orient, Decide, Act
    • Examples of Applications of Cognitive Digital Thread / Digital Twin approaches
Materials

COURSE DELIVERY AND MATERIALS

  • The course lectures will be delivered via Zoom. Access to the Zoom classroom will be provided to registrants near to the course start date.
  • All sessions will be available on demand within 1-2 days of the lecture. Once available, you can stream the replay video anytime, 24/7. All slides will be available for download after each lecture.
  • No part of these materials may be reproduced, distributed, or transmitted, unless for course participants. All rights reserved.
  • Between lectures, the instructor will be available via email for technical questions and comments.
Instructor
Dr. Edward M. “Ed” Kraft is an independent consultant to the Aerospace and Defense industry. He has over 55 years’ experience in testing and evaluation in the government, industry, and academia. He recently retired as a member of the federal Senior Executive Service, serving as the Chief Technologist for ground testing in the Air Force Test Center where he was the top national technical expert for testing systems in aerodynamic, propulsion and space ground test facilities. He is a nationally recognized leader in the innovative integration of high-performance computing with testing to improve defense acquisition. He is one of the initiators and principal architects for the Air Force Digital Thread/Digital Twin initiative. He has presented over a dozen papers on Digital Engineering and has taught numerous short courses on Digital Engineering to the DoW, NASA, US Space Force, DOE, and several aerospace companies. He received a B.S. in Aerospace Engineering from the University of Cincinnati and a Master’s and PhD from the University of Tennessee Space Institute. He is a Distinguished Alumnus of both universities as well as a Fellow of the American Institute of Aeronautics and Astronautics.

 

AIAA Training Links