Tutorials

1. Digital Transformation Foundations with Model-based System Engineering and Digital Engineering

Synopsis:

The landscape of complex system design and engineering stands at a paradigm shift on how future engineered systems will be acquired, designed, manufactured, and fielded. This paradigm shift, referred to as Digital Transformation of Enterprise, proposes digital engineering practice with integrated models across the system life cycle.  Digital Engineering is a holistic approach to system design that replaces documents centric practices with digital models, artifacts, and data; beginning with highly abstract conceptual design models to high fidelity manufacturing, maintenance, and   operational models. Advanced digital engineering capabilities now make it possible to perform a full spectrum system analysis with connected models throughout the system life cycle, such as analyzing the impact of requirements and conceptual design changes on the system manufacturing and sustainment. Model-Based Systems Engineering (MBSE) has become an essential enabler for Digital Transformation and Digital Engineering in the design and development of complex avionics systems, enabling improved traceability, verification, and system integration.  

This tutorial provides a comprehensive introduction to the Digital Transformation Initiative and the evolving Digital Engineering frameworks with a focuses on the role of MBSE via Systems Modeling Language (SysML) as a foundational tool for Digital Engineering in avionics systems. SysML offers a standardized, graphical notation for representing system architecture, behavior, and requirements, facilitating interdisciplinary collaboration and reducing design inconsistencies. Through a structured exploration of SysML diagrams—including requirement, structural, and behavioral views—this tutorial equips engineers with the fundamental knowledge required to implement Digital Engineering methodologies effectively in avionics development. Fundamental concepts of Digital Engineering that include creating digital twins, digital threads, physical twins and multi-fidelity model integration will be introduced.   

Topics Covered and Learning Objectives:  

  • At the successful completion of the tutorial, the participants are expected to:  
  • Discuss the current Digital Transformation and Digital Engineering paradigm shift for complex systems  
  • Articulate what is Digital Engineering and why it is needed  
  • Articulate how Digital Engineering is different from modeling and simulation practices  
  • Recognize common Digital Engineering frameworks and guidelines  
  • Explain the difference between Digital Engineering, MBSE, Digital Twins, Physical Twins, and Digital Threads  
  • Relate MBSE artifacts as enablers for Digital Engineering 
  • Articulate model-fidelity and how it relates to Digital Engineering 

Offered at:


US Army GVSC



NCMS



IEEE



DASC

2. Information Fusion Tutorial

Model-based Systems Engineering of Information Fusion Systems and Evaluation with Machine Learning and Statistical Methods   

Synopsis:  

Information fusion (IF) systems find their application in multiple domains from defense applications to self-driving cars, and autonomous systems. Irrespective of the application domain, an IF system’s objective is to produce optimal state/situation estimates from various sources that are supportive of typically mixed-initiative decision-making which leads to an action. These three processes of fusion, sensemaking, and decision-making have critical interdependencies that are often overlooked in an IF system design. Engineering the IF system requires a holistic, systemic perspective that includes evaluation of a multitude of interacting design variables which span the fusion, sensemaking and decision-making aspects. This tutorial addresses this problem from a Systems Engineering approach and evaluation methodology.    In this tutorial, first the interdependencies between fusion, sensemaking, and decision-making are introduced, followed by a development of a domain-agnostic framework which provides holistic design and performance evaluation of an IF system. A system development framework is presented that leverages Systems Engineering principles and Model-based Systems Engineering (MBSE) techniques for practical system development. On the evaluation side, this tutorial pairs the MBSE approach with statistical methods and machine learning to provide a holistic and integrated evaluation of fusion, sensemaking, and decision-making. Theoretical foundations for performing design and analysis of experiments, followed by a hands-on IF system application example is presented. A refresher on Monte-Carlo simulations and hypothesis testing will also be provided. At the conclusion of the tutorial, the participants will be able to:  
  • Identify and discuss the nature of interdependencies in fusion, sensemaking, and decision-making processes and derive the implications for IF system design 
  • Appreciate the value Systems Engineering and MBSE provide for a systemic design methodology for IF system and decompose the IF system into a set of inter-dependent design variables  
  • Formulate an ‘experimental design’ for the IF system design and performance evaluation 
  • Employ hypothesis testing for comparing uncertain data and perform analysis of variance (ANOVA) to establish statistical significance of design variables and interactions 
  • Perform multiple comparison statistical range tests to quantify the impact of variation and obtain sensitivity analysis amidst interacting design variables 

Offered at:

3. AIAA Professional Development Course

Fundamentals of Data and Information Fusion for Aerospace Systems

Instructors: Dr. James Llinas and Dr. Ali Raz

Synopsis:

This course provides an introductory overview of the concepts, frameworks, representative applications, mathematical sketches, and research issues in the field of Data and Information Fusion. Data and Information Fusion, which spans sensing, tracking and identification, situation assessment, and resource management for decision-making under uncertainty, is becoming prevalent in aerospace systems and is one of the core technologies for enabling autonomous operations. This course is intended for those unfamiliar with the fundamentals and broader implications of these topics and/or are seeking a roadmap and refresher for approaching this topic for aerospace systems research and development. A top-level overview will also be provided in regard to the systems engineering issues for application of these technologies into real-world aerospace applications such as missile defense, command-and-control, air-traffic control, remote-sensing, and autonomous vehicles.

Learning Outcomes:

  • Describe fundamental concepts of Data and Information Fusion
  • Summarize and apply Multi-Level Reference Model of Data and Information Fusion
  • Develop Networked and Distributed Sensor and Data Fusion
  • Recognize Functional Issues and employ Mathematical Techniques
  • Plan and execute Testing and Evaluation of Data Fusion Processes
  • Perform Systems Engineering of Data Fusion-Enabled Systems
  • Describe Aerospace Applications of Data and Information Fusion

Available online and on-demand at AIAA Shaping the future of Aerospace


AIAA