Electrical and Computer Engineering ETDs

Publication Date

Fall 11-30-2023

Abstract

These days large volumes of data can be recorded and manipulated with relative ease. If valuable information can be extracted from them, these vast amounts of data can be a rich resource not just for the digital economy but also for scientific discovery and development of technology. When it comes to deriving valuable information from data, Machine Learning (ML) emerges as the key solution. To unlock the potential benefits of ML to science and technology, extensive research is needed to explore what algorithms are suitable and how they can be applied.

To shine light on various ways that ML can be impactful to engineering workflows, fields of computational EM and antenna design are chosen and efforts are focused on answering three fundamental questions: (1) Is it possible to obtain predictive models from the available simulation and measurement data? (2) What are the domain-specific machine learning algorithms required to convert various datasets to modeling knowledge? (3) Once the modeling knowledge has been learned, how to seamlessly incorporate it into a data-driven predictive environment?

Keywords

AI, Machine Learning, Computational Electromagnetics, FDTD, Antenna Design

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Christos Christodoulou

Second Committee Member

Zhen Peng

Third Committee Member

Edl Schamiloglu

Fourth Committee Member

Jehanzeb H. Chaudhry

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