Introduction to Digital Engineering (1-Day)
This one-of-a-kind course is at the leading edge of the rapidly emerging field of digital engineering.
The course will introduce Digital Engineering strategies and methods to transform system engineering paradigms deploying model-based digital surrogate truth sources, demonstrate the context and value of implementing uncertainty quantification at all levels and phases of decision making under risk, and translating high-volume, high-velocity digital data into better products, processes, and mission effectiveness by delivering essential knowledge to master risk at the speed of need.
SMARTUQ, an uncertainty quantification (UQ) and analytics software tool, will be used for practical illustrations of the context and methods for UQ applications to the creation of digital surrogate truth sources, quantified margins and uncertainties, and support to decision making under risk.
- Gain an in-depth understanding of Digital Engineering concepts and methods
- Translate the DoD Digital Engineering Strategy into practical actions to create lifecycle value
- Create, calibrate, and apply authoritative digital surrogates truth sources
- Implement quantified margins and uncertainties analyses to master risk at critical decision points
- Discover better systems and test engineering performance metrics to support critical decision making under risk
Who Should Attend
This course is intended for decision makers, program managers, chief engineers, test engineers, engineers, analysts, and data scientists from aerospace or any other industry interested in creating business value.
Please contact Jason Cole if you have any questions about courses and workshops at AIAA forums.
- An Introduction to Digital Engineering – a brief history of the development of digital engineering and definition of key terms and concepts
- Translation of the DoD Digital Engineering Strategy into Actionable Tasks for Implementation - identification of a Know-See-Think-Do-Learn Digital Engineering Ecosystem for better decision making under risks
- Using Collaborative Knowledge to Master Risk at the Speed of Need – an introduction to definitions of uncertainty and the range and context of uncertainty quantification (UQ) tools in a Digital Engineering Environment
- An Overview and Demonstration of Uncertainty Quantification Tools and Processes – a deeper understanding of the UQ tools required to support digital engineering with a live demonstration of the SMARTUQ® comprehensive, scalable software
- New Processes and Metrics for Systems Engineering Using Digital Engineering Principles – an introduction to the definition of new system performance metrics for better decision making and the interaction with traditional system engineering maturity measures.
- A New Value Proposition for Test & Evaluation in a Digital Engineering Environment – changing T&E’s value metric to the quantification and planned mitigation of technical uncertainty and the optimization of testing
- Impact of Digital Engineering on Design and Manufacturing – design for variations, manufacturing digital twin, digital tapestry, Material Review Boards processes.
- Insights into Application of Digital Twins – Onboard/Offboard Data Management, Edge Devices, Swarms, the Internet of Things, Big Data Analytics, and Machine Learning
Ed Kraft is the Associate Executive Director for Strategic Initiatives at the University of TN Space Institute. He has over 48 years’ experience in innovative integration of high-performance computing with ground/flight testing. He is of the early pioneers and principal architects for the AF Digital Thread/Digital Twin and continues working across the A&D Industry advancing concepts for Digital Engineering applications. He is an AIAA fellow and Digital Engineering Integration Committee member.
Gavin Jones, SmartUQ Applications Engineering, is responsible for performing simulation and statistical work for clients in aerospace, defense, automotive, gas turbine, and other industries. He is a contributor to SmartUQ’s Digital Thread/Digital Twin initiative.