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AE590 Seminar: A Data-Driven Approach to Systems Engineering-Enabled Complex System Design

Event Type
Seminar/Symposium
Sponsor
Department of Aerospace Engineering
Location
CIF 3039
Date
Nov 27, 2023   4:00 - 5:00 pm  
Speaker
Esma Karagoz, Illinois Institute of Technology
Contact
Courtney McLearin
E-Mail
cmcleari@illinois.edu
Views
86
Originating Calendar
Aerospace Engineering Seminars

Design and development of complex engineered systems in the aerospace industry have been facing challenges in terms of managing ever-increasing complexity. Due to the disconnect in current multidisciplinary design efforts, the behavior of the system may not be accurately predicted, possibly resulting in a divergence between the project outcome and what the designers envisioned. This is one of the most prevalent challenges observed in the aerospace industry, especially in the integration phase of product development, causing budget and schedule overruns. In this talk, I will focus on the integration of multidisciplinary design into model-based systems engineering practices to reduce the system development times and the allocated budgets. I will present a novel system development paradigm that combines semantic, physics-based and machine learning models to improve the decision-making processes throughout the life cycle of a system. I will cover novel algorithms for transforming systems engineering related knowledge into graph databases and a fusion of graph-based neural network algorithms to leverage the design knowledge for more accurate and explainable decisions. This causal-aware and robust methodology I have developed has shown that the integration of heterogeneous domain-specific knowledge is essential for enabling digital implementation of aircraft systems.

 

About the speaker: Esma Karagoz is an assistant professor of aerospace engineering at the Illinois Institute of Technology. Her research centers around model-based systems engineering, and multidisciplinary design and optimization, with the ultimate goal of developing frameworks that integrate physics-based and descriptive models to automate engineering tasks. In her research, she utilizes artificial intelligence and machine learning techniques to understand complex system behavior and improve the decision-making processes throughout the life cycle of systems. Esma holds a Ph.D. degree in aerospace engineering, and M.S. degrees in aerospace engineering and computational science and engineering from the Georgia Institute of Technology. She received her B.S. degree in aerospace engineering from the Middle East Technical University. As a graduate researcher at Georgia Tech, she has contributed to several research projects funded by the industry and government on model-based systems engineering and future aircraft concepts.



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