Systems Engineering and Artificial Intelligence for Aerospace Applications – Online Short Course (Starts 1 Apr 2025) 1 April - 10 April 2025 Online

Register Now

ai-1










  • From 1–10 April 2025 (2 Weeks, 4 Lectures/Classes, 8 Total Hours)
  • Every Tuesday and Thursday from 1–3 p.m. Eastern Time (all sessions will be recorded and available for replay; course notes will be available for download)
  • Advance your expertise in AI for aerospace with this first course in the AIAA series, focusing on building systems with Artificial Intelligence and using AI to enhance the systems engineering process.
  • All students will receive an AIAA Certificate of Completion at the end of the course.

OVERVIEW

This is the first in a series of AIAA Artificial Intelligence short courses focusing on Responsible AI. These courses are tailored to equip aerospace professionals with the essential knowledge, skills, and analytical abilities to tackle the challenges of responsibly designing and deploying AI-integrated systems. As AI becomes increasingly embedded in aerospace, it presents significant opportunities for efficiency, cost reduction, and safety enhancement. However, recognizing and mitigating the associated risks is essential to ensure the safety, reliability, and ethical integrity of these technologies.

Participants in these courses will gain a solid foundation in AI fundamentals, learn how to architect AI systems, understand the principles of systems engineering as they apply to AI, understand and evaluate ethical and societal implications, and manage AI risk. Graduates will be well-prepared to develop and manage complex systems with embedded AI, including identifying unique requirements, testing, and certifying these systems, and maintaining safe performance levels. This is particularly crucial for safety-critical systems, such as self-flying vehicles and drone delivery, where rigorous testing, evaluation, monitoring, and maintenance is paramount.

This course provides a foundation for systems engineers to understand the implications of both building systems with Artificial Intelligence (SE for AI) and using Artificial Intelligence to enhance the systems engineering process (AI for SE). The course introduces the foundations of AI, including different types of machine learning, and the associated design, test, and evaluation challenges for AI systems. AI opportunities for transforming SE lifecycle activities are discussed along with applications of AI in modern systems.
LEARNING OBJECTIVES
  • Deepen understanding of AI Foundations and AI Intensive Systems: Develop a foundation in artificial intelligence principles, including various types of machine learning, and how they apply to aerospace systems.
  • Define the Requirements and Risk for AI Systems: Applying strong systems engineering to specify and assess the implementation of AI in larger systems.
  • Explore the Challenges of Fielding AI Systems: Using systems engineering to inform Ai Component Definition and Integration, Testing, Verification, and Validation
  • Examine AI in Aerospace Opportunity: Survey the wide breadth and possibility space for AI systems in aerospace applications.

AUDIENCE
Professionals tasked with driving the safe and effective integration of AI within their organizations, who are eager to enhance their expertise in the design, testing, and deployment of cutting-edge AI-based aerospace technologies.

COURSE FEES (Sign-In to Register)
- AIAA Member Price: $495 USD
- Non-Member Price: $695 USD
- AIAA Student Member Price: $295 USD

LEARNING TRACK: Responsible AI in Aerospace

  • While courses can be taken on their own, students can bundle all four courses in the Responsible AI in Aerospace Learning Track with significant discounts.
  • Students that attend all 4 courses will receive an additional AIAA Certificate of Completion.

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 (2 hours)
Introduction to Systems Engineering and AI-Intensive Systems

Class 2 (2 hours)
Requirements Engineering and Risk for AI-Intensive Systems

  • Topics:
    • Eliciting and specifying requirements for AI-Intensive systems
    • Risk identification, assessment, and mitigation strategies for AI systems
    • Use Case Illustration of Requirements and Risk Analysis
  • Critical Reading and Discussion:
    • H. Belani, M. Vukovic and Ž. Car, "Requirements Engineering Challenges in Building AI-Based Complex Systems," 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), Jeju, Korea (South), 2019, pp. 252-255,
    • https://arxiv.org/pdf/1908.11791.pdf

Class 3 (2 hours)
Fielding the AI Intensive System

Class 4 (2 hours)
How AI is Changing Aerospace Engineering

  • Topics:
    • Part design
    • Inspection processes,
    • Route optimization and air traffic management
  • Critical Reading and Discussion:
    • Hassan, K., Thakur, A.K., Singh, G. et al. Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review. Arch Computat Methods Eng31, 4031–4086 (2024).
    • https://doi.org/10.1007/s11831-024-10105-7

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. Videos will be until 10 May 2025.
  • 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 during the course, the instructor(s) will be available via email for technical questions and comments.
Instructors
Mr. Phil Barry is a strategic and technical leader with a robust background in systems engineering, military simulation, and space mission-critical systems. As the Director of the Mission Analysis Group at the L3Harris Corporation, he leads large-scale modeling, simulation, and analysis projects to deliver key capabilities for the DoD and the Intelligence Community. Mr. Barry is also an Adjunct Professor at George Mason University where he teaches courses in the Systems Engineering of Artificial Intelligence, Heterogeneous Data Fusion, Decision Analysis, and Project Management. He has a Ph.D. in Information Technology, an MS in Systems Engineering, and a BS in Aerospace Engineering.

 

AIAA Training Links